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[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms.", "The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways.", "In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations.", "Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases.", "This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018 . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath.", "This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway such as chronic rhinosinusitis, CRS and lower airway such as asthma and chronic obstructive pulmonary disease, COPD which greatly affect the patients' quality of life Calus et al., 2012; Bao et al., 2015 . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease.", "Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness Xepapadaki and Papadopoulos, 2010 . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 .", "Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease Hashimoto et al., 2008; Viniol and Vogelmeier, 2018 . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers.", "However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018 . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath.", "These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019 . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 .", "Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019 . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases Rowan et al., 2015; Tan et al., 2017 . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection .", "One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection . . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 .", "In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017 . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation.", "While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway.", "To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s .", "Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s . ; with bacterial infections previously considered as the likely culprit for acute exacerbation Stevens, 1953; Message and Johnston, 2002 . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 .", "However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s Message and Johnston, 2002 . Rhinovirus RV and respiratory syncytial virus RSV are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases Jartti and Gern, 2017 . Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 .", "Other viruses such as parainfluenza virus PIV , influenza virus IFV and adenovirus AdV have also been implicated in acute exacerbations but to a much lesser extent Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019 . More recently, other viruses including bocavirus BoV , human metapneumovirus HMPV , certain coronavirus CoV strains, a specific enterovirus EV strain EV-D68, human cytomegalovirus hCMV and herpes simplex virus HSV have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 .", "The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway Mallia and Johnston, 2006; Britto et al., 2017 . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche .", "During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche . . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells.", "In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection Vareille et al., 2011; Braciale et al., 2012 . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 .", "However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019 . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019 . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity.", "Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation.", "The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases.", "This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection.", "Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I IFNα/β and type III IFNλ interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α MIP-1α and monocyte chemotactic protein 1 MCP-1 Wark and Gibson, 2006; Matsukura et al., 2013 . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 .", "These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells APCs that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon IFNγ , IL-2, IL-4, IL-5, IL-9, and IL-12 Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012 . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells Wark and Gibson, 2006; Braciale et al., 2012 . The increased inflammation, in turn, worsens the symptoms of airway diseases.", "The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp CRSwNP , viral infections such as RV and RSV promote a Type 2-biased immune response Becker, 2006; Jackson et al., 2014; Jurak et al., 2018 . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 .", "This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015 . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 .", "On the other hand, patients with COPD and patients with CRS without nasal polyp CRSsNP are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 Cukic et al., 2012; Brightling and Greening, 2019 . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases MMPs released from infiltrating neutrophils . .", ". Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019 . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 .", "An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin TSLP , and their interaction with group 2 innate lymphoid cells ILC2 has also recently been identified Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019 . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier Gabryelska et al., 2019; Roan et al., 2019 . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 .", "ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated Scanlon and McKenzie, 2012; Li and Hendriks, 2013 . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019 . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation .", "These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation . . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation .", "In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation . . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways .", "Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals Yan et al., 2016; Tan et al., 2018a ; despite augmenting a type 2 exacerbation in chronically inflamed airways . . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 .", ". These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases.", "As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance.", "Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway.", "The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | mechanisms have been implicated in acute exacerbations during and after viral infection . . Murray et al. .", ". Murray et al. . has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway.", "has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance Kim et al., 2008; Stolz et al., 2019 in particular the late onset of a bacterial infection Singanayagam et al., 2018 Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections.", "In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the \"united airway\" hypothesis . .", ". On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Hence, their presence may further alter the local environment and contribute to current and future exacerbations.", "Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases.", "In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 .", "Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells Becker, 2006; McKendry et al., 2016 . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019 . Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 .", "Additionally, there are also evidence of reduced type I IFNβ and III IFNλ interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium Contoli et al., 2006; Hwang et al., 2019; Wark, 2019 . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation Wood et al., 2011; Essaidi-Laziosi et al., 2018 . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms.", "These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer Imperiale and Jiang, 2015 , further contributing to chronic activation of inflammation when they infect the airway Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017 . With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 .", "With that note, human papilloma virus HPV , a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015 . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 .", "Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection Chi et al., 2011; Ford et al., 2013; Papi et al., 2013 . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection.", "The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV Yan et al., 2016; Guibas et al., 2018 and certain CoV including the recently emerged COVID-19 virus Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020 , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 .", "These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection Yan et al., 2016; Tan et al., 2019 . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium Ampomah et al., 2018; Tan et al., 2019 . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium .", "Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway . .", ". Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors .", "Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation.", "Fortunately, annual vaccines are available to prevent IFV infections Vasileiou et al., 2017; Zheng et al., 2018 ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration.", "Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 .", "For example, IFV infection was found to induce oncostatin M OSM which causes tight junction opening Pothoven et al., 2015; Tian et al., 2018 . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018 . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 .", "In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 ANGPTL4 and bactericidal/permeabilityincreasing fold-containing family member A1 BPIFA1 are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018 . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome.", "Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment . .", ". In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019 . In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 .", "In addition, viral infection may disrupt biofilm colonies in the upper airway e.g., Streptococcus pneumoniae microbiome to be release into the lower airway and worsening the inflammation Marks et al., 2013; Chao et al., 2014 . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth Siegel et al., 2014; Mallia et al., 2018 . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 .", "Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms Singanayagam et al., 2018 Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019 . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles . .", ". These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases Wark et al., 2013; Singanayagam et al., 2018 . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome .", "To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome . . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection.", "Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases.", "Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations.", "Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations.", "The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation.", "The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection.", "Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism with literature evidence Oxidative stress ROS production RV, RSV, IFV, HSV As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations .", "However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation see text for abbreviations . that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection . .", ". Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown Peng et al., , 2019 Qiu et al., 2018 . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b .", "Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 DNAH5 and multiciliate differentiation And DNA synthesis associated cell cycle protein MCIDAS Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model . .", ". Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation .", "In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation . . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage.", "Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases .", "MicroRNAs miRNAs are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases . . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 .", "miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018 . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases . .", ". Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids .", "Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids . . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 .", "IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections Hsu et al., 2016 Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations . .", ". Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018 . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases.", "Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 .", "Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes Cardenas et al., 2019; Gomez, 2019 . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019 . In addition, Spalluto et al.", "In addition, Spalluto et al. . also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases . .", ". Finally, viral infection can result in enhanced production of reactive oxygen species ROS , oxidative stress and mitochondrial dysfunction in the airway epithelium Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018 . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 .", "The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway Barnes, 2017; van der Vliet et al., 2018 . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018 . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region.", "Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway . .", ". A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients.", "While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms.", "Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing RNA-seq or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a .", "Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019 . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 .", "Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition Ito et al., 2019; Li and Di Santo, 2019 . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations.", "With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease.", "In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms.", "New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment." ]

[ "Hantaan virus HTNV causes hemorrhagic fever with renal syndrome HFRS . Previous studies have identified interferon-induced transmembrane proteins IFITMs as an interferon-stimulated gene family. However, the role of IFITMs in HTNV infection is unclear.", "However, the role of IFITMs in HTNV infection is unclear. In this study, we observed that IFITM3 single nucleotide polymorphisms SNP rs12252 C allele and CC genotype associated with the disease severity and HTNV load in the plasma of HFRS patients. In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity.", "In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity. We also proved that IFITM3 was able to inhibit HTNV infection in both HUVEC and A549 cells by overexpression and RNAi assays, likely via a mechanism of inhibiting virus entry demonstrated by binding and entry assay. Localization of IFITM3 in late endosomes was also observed.", "Localization of IFITM3 in late endosomes was also observed. In addition, we demonstrated that the transcription of IFITM3 is negatively regulated by an lncRNA negative regulator of interferon response NRIR . Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS.", "Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS. Text: associates with the severity of disease, indicating the importance of viremia in the pathogenesis of HFRS . .", ". Therefore, further studies of host factors limiting HTNV infection and influencing antiviral response as well as disease progression are clinically significant and timely. The human family of interferon-induced transmembrane proteins IFITMs was discovered 25 years ago to consist of interferon-stimulated genes ISGs . .", ". This family includes five members, namely, IFITM1, 2, 3, 5, and 10, among which IFITM1, 2, and 3 possess antiviral activity . . Different IFITM proteins have different antiviral spectrum . . For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice .", "For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice . , and it also inhibits multiple viruses, including filoviruses, rhabdoviruses, flaviviruses, and even Ebola and Zika virus . . . . . . The antiviral mechanism of IFITM3 is thought to be the restriction of viral entry into cells . .", ". Single nucleotide polymorphisms SNPs are single nucleotide variations in a genetic sequence that occur at an appreciable frequency in the population. Several SNPs has been identified in IFITM3, among which the rs12252 site with C allele results in a N-terminal truncation of IFITM3 protein, leading to impaired inhibition of influenza virus in vitro . .", ". Notably, the frequencies of rs12252 C allele and CC genotype correlate with disease severity in patients infected with influenza virus . . HTNV has been shown to induce a type I interferon response though in later time postinfection . . While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection .", "While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection . , however, the effect of IFITMs on HTNV infection in human cell lines and its role in HFRS still remain unknown. LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators.", "LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators. Some lncRNAs have been shown to play a role in innate immunity . . Among them, negative regulator of interferon response NRIR lncRNA NRIR, also known as lncRNA-CMPK2 is a non-coding ISG that negatively regulates IFITM1 and Mx1 expression in HCV infection . .", ". Notably, IFITM3 is largely homologous to IFITM1, but the role of NRIR in the regulation of IFITM3 in HTNV infection remains unclear. In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans.", "In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans. We provide primary evidence suggesting that IFITM3, regulated by NRIR, can inhibit HTNV infection and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection.", "This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection. This study was conducted in accordance with the recommendations of the biomedical research guidelines involving human participants established by the National Health and Family Planning Commission of China. The Institutional Ethics Committee of Tangdu Hospital approved this study.", "The Institutional Ethics Committee of Tangdu Hospital approved this study. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained.", "Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained. Sixty-nine HFRS patients admitted into the Department of Infectious Diseases, Tangdu Hospital between October 2014 and March 2016 were enrolled in this study. All patients were Han Chinese.", "All patients were Han Chinese. The diagnosis of HFRS was made based on typical symptoms and signs as well as positive IgM and IgG antibodies against HTNV in the serum assessed by enzyme linked immunosorbent assay ELISA in our department. The classification of HFRS severity and the exclusion criteria were described as follows .", "The classification of HFRS severity and the exclusion criteria were described as follows . : white blood cells WBC , platelets PLT , blood urea nitrogen BUN , serum creatinine Scr , and heteromorphic lymphocytes that were tested by the Department of Clinical Laboratory shown in Table 1 . According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described .", "According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described . : . mild patients were identified with mild renal failure without an obvious oliguric stage; .", "mild patients were identified with mild renal failure without an obvious oliguric stage; . moderate patients were those with obvious symptoms of uremia, effusion bulbar conjunctiva , hemorrhage skin and mucous membrane , and renal failure with a typical oliguric stage; . severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and .", "severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and . critical patients exhibited ≥1 of the following signs during the illness: refractory shock, visceral hemorrhage, heart failure, pulmonary edema, brain edema, severe secondary infection, and severe renal failure with oliguria urine output, 50-500 ml/day for >5 days, anuria urine output, <50 ml/day for >2 days, or a BUN level of >42.84 mmol/l. Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group.", "Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group. The exclusion criteria for this study were patients with: . any other kidney disease, . diabetes mellitus, . autoimmune disease, . hematological disease, .", "diabetes mellitus, . autoimmune disease, . hematological disease, . cardiovascular disease, . viral hepatitis types A, B, C, D, or E , or . any other liver disease. In addition, no patients received corticosteroids or other immunomodulatory drugs during the study period . .", ". Genomic DNA was extracted from the peripheral blood of patients using the PureGene DNA Isolation kit Gentra Systems, Minneapolis, MN, USA . The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ .", "The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ . The PCR products were purified and sequenced using an Applied Biosystems 3730xl DNA Analyzer Thermo Scientific, Waltham, MA, USA . The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods .", "The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods . . Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA .", "Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA . The SuperScript III Platinum One-Step Quantitative RT-PCR System kit Invitrogen, Carlsbad, CA, USA was employed for the real-time RT-PCR assay. The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′.", "The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′. The synthetic S segment of the HTNV standard strain 76-118 RNA transcript was used as the quantitative calibrator. The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators.", "The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators. For each experiment, one aliquot of calibrated 76-118 standard was extracted in parallel with the clinical samples and serially 10-fold diluted with concentrations ranging from 10.56 to 2.56 log10 copies/ml. PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C.", "PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C. Fluorescence was read during the 72°C step of the final segment of every cycling program. HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator.", "HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator. A549 cells ATCC Cat# CRM-CCL-185, RRID:CVCL_0023 were grown in our laboratory in DMEM with 10% FBS Thermo Scientific, Waltham, MA, USA in a 5% CO2 incubator. Cells were used within passage 10 after primary culture.", "Cells were used within passage 10 after primary culture. HTNV strain 76-118 was cultured in Vero E6 cells ATCC Cat# CRL-1586, RRID:CVCL_0574 in our laboratory and titrated using an immunofluorescence staining assay for HTNV nucleocapsid protein NP as previously described . . The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method.", ". The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method. The recombinant human IFN-α2a was obtained from PBL Interferon Source Piscataway, NJ, USA and dissolved in the buffer provided by the manufacturer composition not disclosed . HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins.", "HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins. Subsequently, the virus solution was removed and fresh medium added to the cell culture. Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10.", "Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10. Puromycin 2 μg/ ml for HUVEC and 6 μg/ml for A549 cells was used to create cell lines stably expressing IFITMs. Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA .", "Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA . SiRNAs were purchased from Origene Rockville, MD, USA , and the sequences were not disclosed. Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA .", "Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA . Quantitative realtime PCR qPCR was performed using SYBR Premix Ex Taq II Takara Biotechnology Co., Dalian, China with a Bio-Rad iQ5 cycler Bio-Rad, Hercules, CA, USA . β-actin was used as the reference gene.", "β-actin was used as the reference gene. The primers Sangon Biotech, Shanghai, China were as follows: IFITM1 forward, 5′-ACTCCGTGAAGTCTAGGGACA-3′ and reverse, 5′-TGTCACAGAGCCGAATACCAG-3′ ; IFITM2 forward, 5′-ATCCCGGTAACCCGATCAC-3′ and reverse, 5′-CTTCCTGTCCCTAGACTTCAC-3′ ; IFITM3 forward, 5′-GGTCTTCGCTGGACACCAT-3′ and reverse, 5′-TGTCCCTAGACTTCACGGAGTA-3′ ; IFITM3 pre-mRNA forward, 5′-CATAGCACGCGGCTCT CAG-3′ and reverse, 5′-CGTCGCCAACCATCTTCCTG-3′ ; HTNV S segment forward, 5′-GCCTGGAGACCATCTGA AAG-3′ and reverse, 5′-AGTATCGGGACGACAAAGGA-3′ ; β-actin forward, 5′-GCTACGTCGCCCTGGACTTC-3′ and reverse, 5′-GTCATAGTCCGCCTAGAAGC-3′ ; NRIR forward, 5′-ATGGTTTTCTGGTGCCTTG-3′ and reverse, 5′-GGAGGTTAGAGGTGTCTGCTG-3′ ; NRAV forward, 5′-TCACTACTGCCCCAGGATCA-3′ and reverse, 5′-GGTGGTCACAGGACTCATGG-3′ . For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA .", "For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . MiR-130a level was determined using the gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control .", "U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control . . Because the pre-mRNA levels can represent the initial transcription rate . , the primers used to detect the pre-mRNA of IFITM3 were designed targeting the intron of IFITM3 as previously described . . IFITM3 has two exons and one intron.", ". IFITM3 has two exons and one intron. For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon.", "For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon. For qPCR of IFITM3 mRNA, the forward primers were positioned in the first exon, and the reverse primer was positioned at the beginning of the second exon . .", ". Because the basal expression of IFITM3 is low in A549 cells, we detected IFITM3 mRNA and pre-mRNA in A549 cells following IFN-α2a treatment 20 IU/ml for 12 h after the overexpression of NRIR. Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA .", "Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA . Equal amounts of protein 20 μg protein/lane were electrophoresed on a 10%-SDS-polyacrylamide gel and electrophoretically transferred to a polyvinylidene difluoride membrane Millipore, Billerica, MA, USA . After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C.", "After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C. The membranes were then washed and incubated with HRP-conjugated IgG antibody Cell Signaling Technology, Danvers, MA, USA for 1 h at room temperature. The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film.", "The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film. The blot densities were analyzed using the Quantity One software Bio-Rad, Hercules, CA, USA . In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS.", "In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS. Protease inhibitor cocktail Roche, Basel, Switzerland was added before use. The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 .", "The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 . At the indicated times post-HTNV infection, the cells were fixed with 4% PFA, incubated with 0.3% Triton X-100 Sigma-Aldrich, St. Louis, MO, USA , and blocked with 5% BSA for 1 h. Following incubation with a mouse monoclonal antibody against c-myc-tag Sigma-Aldrich, St. Louis, MO, USA, Sigma-Aldrich Cat# M5546 , IFITM3, lysosome-associated membrane glycoprotein 1 LAMP1, Cell Signaling Technology, Danvers, MA, USA , or HTNV NP at 37°C for 2 h, the cells were washed and incubated with anti-rabbit Ig conjugated to Alexa 555 and anti-mouse Ig conjugated to Alexa 488 Abcam, Cambridge, MA, USA secondary antibodies at room temperature for 1 h. The nuclei were counterstained with DAPI. An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images.", "An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images. hTnV binding and entry assay Cells transduced with IFITM3 or the empty vector were detached and washed extensively with cold PBS. The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation.", "The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation. Part of cells were washed extensively with ice-cold PBS and harvested for binding assay. Another part of cells were switched to 37°C for 2 h to allow HTNV entry.", "Another part of cells were switched to 37°C for 2 h to allow HTNV entry. The HTNV that remained on the cell surface was removed by treatment with proteinase K 0.1 mg/ml, Thermo Scientific, Waltham, MA, USA . To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl.", "To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl. Adsorption of HTNV moi = 1 was performed at 4°C for 1 h. The cells were then washed, and fusion of the virus with the plasma membrane was triggered by incubation in low pH medium 20 mM sodium succinate, pH = 5.5 for 10 min at 37°C. Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl .", "Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl . . qPCR analysis of the HTNV S segment was conducted to evaluate the influence of IFITMs on HTNV cell adhesion and HTNV entry. All data were expressed as the mean ± SEM.", "All data were expressed as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 GraphPad Software, La Jolla, CA, USA . For association analysis of the rs12252 allele and genotype, Fisher's exact test was used. Independent samples t-tests were used for normally distributed data.", "Independent samples t-tests were used for normally distributed data. Differences among groups were determined by one-way analysis of variance ANOVA with repeated measures, followed by Bonferroni's post hoc test. P < 0.05 was considered statistically significant.", "P < 0.05 was considered statistically significant. The iFiTM3 snP rs12252 c allele and cc genotype associated with severe hFrs Disease and a higher Plasma hTnV load To determine the clinical significance of IFITM3 SNP in HTNV infection, the relationship between rs12252 SNP and the severity of HFRS in 69 patients were examined. We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients.", "We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients. Then, we stratified these patients into mild and severe, based on the clinical assessment as described in Section \"Material and Methods. \" We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 .", "We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 . The frequency of rs12252 C in severe patients was also higher than those mild patients 68.29 vs. 46.43%, P = 0.013, Figures 1A,B; Table 2 . These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 .", "These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 . For genotypes, 43.90% of the severe patients carried the CC genotype, a significantly higher frequency than the control Han Chinese per 1,000 genomes sequence database 26.92% CC genotype, P = 0.03 as well as mildly infected patients 14.29%, P = 0.02, Figures 1A,B ; Table 2 . However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity.", "However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity. c The plasma HTNV load in CC genotype patients and CT/TT genotype patients, tested by qRCR analysis. Each symbol represents one individual patient.", "Each symbol represents one individual patient. Independent samples t-test was used to test the difference of HTNV load between groups. *P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population.", "*P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population. In addition, we also found that patients with CC genotype had higher plasma viral load in acute phase Figure 1C . These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS.", "These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS. Previous studies reveal that the truncated IFITM3 protein produced by SNP rs12252 C allele Figure 2A , the missing part stands for the truncated 21 amino acids from N-terminal of IFITM3, the intramembrane helix, and transmembrane helix was presented as boxes leads to an impaired anti-influenza activity . .", ". To test the functional significance of this polymorphism in HTNV infection, we transfected the majority T or minority C variant IFITM3 alleles that produce full-length or N-terminally truncated NΔ21 proteins Figure 2A with c-myc-tag to HUVEC and A549 cell using lentivirus vectors Figure 2B . Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material .", "Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material . Indeed, compared with the mock empty vector -infected control, the NΔ21 protein almost lost the ability to inhibit HTNV infection in both HUVEC and A549 cells Figures 2C,D ; Figure S3 in Supplementary Material . To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material .", "To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material . While we observed only a moderate upregulation of IFITM1, 2, and 3 mRNA and protein in HUVECs after more than 24 h postinfection; IFITM1, 2, and 3 mRNA, however, were only transiently upregulated in A549 cells and caused no significant change in protein level. We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually.", "We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually. The effect of siRNAs on the expression of target IFITMs was tested by qPCR in HUVECs Figure S2 in Supplementary Material , and the effect of the best oligo against each IFITMs IFITM1C, IFITM2A, IFITM3B was tested by Western blot in A549 Figure 4A and HUVEC cells Figure 4B . To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h .", "To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h . The cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were significantly suppressed in both HUVEC and A549 cells in response to IFN-α2a treatment. Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells.", "Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells. Knockdown of IFITM1 also partially restored the HTNV level in A549 cells Figures 4C,D . These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection.", "These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection. c-myc-tagged IFITM1, 2, and 3 were expressed in both HUVEC and A549 cells Figure 5A , and the cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were suppressed by IFITM3 overexpression in HUVEC cells Figures 5B-D . They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D .", "They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D . The inhibitory effect of IFITM3 was further confirmed by immunofluorescence analysis of HTNV NP Figure S3 in Supplementary Material . These results were in accordance with the above-described RNAi results.", "These results were in accordance with the above-described RNAi results. To determine whether IFITM3 inhibited HTNV binding or entry, HUVEC and A549 cells were incubated with HTNV moi = 1 at 4°C for 1 h, unbound virus was washed away, and HTNV RNA collected at this time point represents HTNV bound to the cell surface. After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus.", "After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus. As a positive control for inhibition of virus entry, we incubated a parallel group of cells with HTNV at pH = 5.5 as described in Section \"Materials and Methods.\" Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B .", "Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B . iFiTM3 Was Partially localized to laMP1 + late endosomes in the host cells To elucidate the mechanism of IFITM3 function, we investigated the subcellular localization of IFTIM3 in the host cells. IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C .", "IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C . The co-localization of IFITM3 and LAMP1 + late endosomes had also been found in A549 cells . . Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry .", "Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry . , this result provides an evidence for the anti-HTNV mechanism of IFITM3. LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies.", "LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies. We tested the change of previously reported regulators of IFITMs, such as NRAV, NRIR, and miR-130a after HTNV infection, among which NRIR was the only changed one downregulated after HTNV infection Figure 7A ; Figure S4 in Supplementary Material in HUVEC. However, the expression of NRIR was unchanged in A549 cells.", "However, the expression of NRIR was unchanged in A549 cells. We overexpressed NRIR in HUVEC and A549 cells using the pcDNA3.1 vector Figure 7B . Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E .", "Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E . These data suggest that lncRNA NRIR is a negative regulator of IFITM3 transcription. Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae.", "Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae. It causes HFRS, which is an important threat to public health worldwide. It is also a potential weapon for biological terrorism. Reservoir animals, usually rodents, are asymptomatic during persistent infection.", "Reservoir animals, usually rodents, are asymptomatic during persistent infection. Unlike in rodents, Hantavirus infection leads to HFRS and Hantavirus pulmonary syndrome HPS in humans . . The major clinical characteristics of HFRS include fever, hemorrhage, hypotension, and renal injury . , causing severe manifestations and death in some cases.", ", causing severe manifestations and death in some cases. The current standard of care for HFRS relies on symptomatic and supportive treatment. It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS ..", "It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS .. However, no direct antiviral medications are currently available for this illness. Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases.", "Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases. It has been reported that HTNV infection induces a late type I interferon response . . However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified.", "However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified. Therefore, identification of ISGs that are effective against HTNV is an attractive strategy to identify novel therapeutic targets. In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese.", "In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese. The rs12252 C allele and CC genotype are also found to be associated with higher plasma viral load in the early stage of HFRS. We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity.", "We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity. Interestingly, IFITM3 is found to restrict HTNV infection with a mechanism of cellular entry inhibition. Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry.", "Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry. In addition, we find that HTNV infection downregulated lncRNA NRIR 48 h post infection, which negatively regulates the transcription of IFITM3. Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS.", "Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. The antiviral properties of IFITM proteins were identified in 2009 in an RNAi screen for host factors that influence influenza virus replication . .", ". IFITM1, 2, and 3 have been demonstrated to possess antiviral activity in several studies. Everitt et al. demonstrated that the severity of influenza virus infection was greatly increased in IFITM3-knockout mice compared with wild-type animals . .", ". Different IFITM members have also been confirmed to inhibit the cellular entry of multiple virus families including filoviruses, rhabdoviruses, and flaviviruses 7, . . . 30 . For example, HIV-1 and HCV infection are inhibited by IFITM1 . . . . . It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry .", "It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry . . Recent studies suggested that the cellular location of different IFITMs may influence the range of viruses restricted by each protein . . IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles .", "IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles . , whereas IFITM3 confines influenza virus in acidified endosomal compartments . .", ", whereas IFITM3 confines influenza virus in acidified endosomal compartments . . Notably, retrovirus subvirus particles ISVPs , which do not require endosomal acidification for entry, are not inhibited by IFITM3 expression, suggesting that IFITM3 may function at the stage of endosomal entry . .", ". Studies utilizing cell-cell fusion assays have suggested that IFITM3 blocks the entry of enveloped virus by preventing the fusion of the viral membrane with a limiting membrane of the host cell, either the plasma membrane and/or the endosomal membranes. The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes .", "The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes . . In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells.", "In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells. Overexpression of IFITM3 inhibited HTNV infection to HUVEC and A549 cells. IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells.", "IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells. All these results suggest that IFITM3 is an important control factor under natural infection of HTNV. Our results also demonstrate that the effectiveness of IFITM3 is cell type-independent, which is in accordance with the results from similar viruses, such as RVFV . .", ". Binding and entry assays, conducted by controlling the temperature and pH, showed that IFITM3 did not significantly influence HTNV binding but inhibited HTNV entry into HUVEC and A549 cells. Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry.", "Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry. However, we failed in tracking the transportation of HTNV in infected cells possibly due to the lack of fluorescence-labeled virus. In addition, IFITM1 also suppressed HTNV infection in A549 cells.", "In addition, IFITM1 also suppressed HTNV infection in A549 cells. The mechanism underlying anti-HTNV effect of IFITM1 remains undetermined and deserves to be further explored. According to a recent study on the three-dimensional structure of IFITM3, there is a C-terminal transmembrane α-helix and a two-N-terminal intramembrane α-helices shown in Figure 2A as black boxes in IFITM3 . .", ". There are two splice variants that differ by the presence or absence of the first N-terminal 21 amino acids deleted part, shown in Figure 2A as red dotted line . Several SNPs including 13 non-synonymous, 13 synonymous, 1 in-frame stop, and 1 splice site acceptoraltering have been reported in the translated IFITM3 sequence . .", ". Among them, the rare SNP rs12252C allele of IFITM3 truncates the protein as described above, leading to a reduced inhibition of influenza virus infection in A549 cells . . We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro.", "We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro. In Northern European patients hospitalized with seasonal influenza or pandemic influenza A virus, increased homozygosity of the minor C allele of SNP rs12252 in IFITM3 was observed . .", ". In Chinese patients infected with influenza A H1N1 virus, there was also an increased frequency of the C allele and CC genotype of SNP rs12252 . . In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients.", "In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients. Patients carrying the CC genotype also had higher plasma viral loads compared with those with the CT/TT genotype. Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo.", "Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo. We speculate that the much higher level of CC allele at healthy population of Han Chinese compared with Caucasians may place the Chinese at a higher risk for developing severe illness upon HTNV infection, which needs further investigation. LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity.", "LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity. For example, lncRNA NEAT1 is reported to be upregulated by influenza virus or PolyI:C stimulation, which promotes IL-8 expression . .", ". lncRNA NRAV has been shown to negatively regulate the initial transcription of IFITM3 and Mx1 by affecting the histone modification of these genes . . lncRNA NRIR is a non-coding ISG, which has been reported to negatively regulate IFITM1 and Mx1 expression in HCV infection . . Mir-130a was also reported as a regulator of IFITM1 . .", ". Mir-130a was also reported as a regulator of IFITM1 . . In this analysis, lncRNA NRIR was downregulated in HUVECs after HTNV infection for 48 h, overexpression of NRIR negatively regulates the initial transcription of IFITM3, evidenced by the decreased pre-mRNA as well as mRNA levels. NRIR overexpression also facilitated HTNV infection.", "NRIR overexpression also facilitated HTNV infection. These results indicate that the downregulation of NRIR after HTNV infection is possibly involved in the activation of innate immune responses against HTNV infection. We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study.", "We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study. Another lncRNA that can regulate IFITM3, i.e., NRAV NR_038854 , remained unchanged after HTNV infection Figures S4A,B in Supplementary Material . Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material .", "Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material . In conclusion, this study revealed a critical role for IFITM3 in HTNV infection. We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells.", "We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells. In addition, we discovered that the IFITM3 SNP rs12252 C allele and CC genotype correlates with the plasma HTNV load and the severity of HFRS; and the rs12252 C allele produces a truncated IFITM3 protein NΔ21 that attenuates its anti-HTNV function. These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease.", "These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease. aUThOr cOnTribUTiOns ZX-y, BP-y, YC-t, and MH-w performed the experiments; WP-z, BX-f, LY-f, ZY, and JZ-s designed the research; HC-x, YW, and WX analyzed the data; TK and ZC-m provided clinical data; ZX-y and BP-y wrote the paper." ]

[ "Hantaan virus HTNV causes hemorrhagic fever with renal syndrome HFRS . Previous studies have identified interferon-induced transmembrane proteins IFITMs as an interferon-stimulated gene family. However, the role of IFITMs in HTNV infection is unclear.", "However, the role of IFITMs in HTNV infection is unclear. In this study, we observed that IFITM3 single nucleotide polymorphisms SNP rs12252 C allele and CC genotype associated with the disease severity and HTNV load in the plasma of HFRS patients. In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity.", "In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity. We also proved that IFITM3 was able to inhibit HTNV infection in both HUVEC and A549 cells by overexpression and RNAi assays, likely via a mechanism of inhibiting virus entry demonstrated by binding and entry assay. Localization of IFITM3 in late endosomes was also observed.", "Localization of IFITM3 in late endosomes was also observed. In addition, we demonstrated that the transcription of IFITM3 is negatively regulated by an lncRNA negative regulator of interferon response NRIR . Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS.", "Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS. Text: associates with the severity of disease, indicating the importance of viremia in the pathogenesis of HFRS . .", ". Therefore, further studies of host factors limiting HTNV infection and influencing antiviral response as well as disease progression are clinically significant and timely. The human family of interferon-induced transmembrane proteins IFITMs was discovered 25 years ago to consist of interferon-stimulated genes ISGs . .", ". This family includes five members, namely, IFITM1, 2, 3, 5, and 10, among which IFITM1, 2, and 3 possess antiviral activity . . Different IFITM proteins have different antiviral spectrum . . For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice .", "For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice . , and it also inhibits multiple viruses, including filoviruses, rhabdoviruses, flaviviruses, and even Ebola and Zika virus . . . . . . The antiviral mechanism of IFITM3 is thought to be the restriction of viral entry into cells . .", ". Single nucleotide polymorphisms SNPs are single nucleotide variations in a genetic sequence that occur at an appreciable frequency in the population. Several SNPs has been identified in IFITM3, among which the rs12252 site with C allele results in a N-terminal truncation of IFITM3 protein, leading to impaired inhibition of influenza virus in vitro . .", ". Notably, the frequencies of rs12252 C allele and CC genotype correlate with disease severity in patients infected with influenza virus . . HTNV has been shown to induce a type I interferon response though in later time postinfection . . While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection .", "While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection . , however, the effect of IFITMs on HTNV infection in human cell lines and its role in HFRS still remain unknown. LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators.", "LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators. Some lncRNAs have been shown to play a role in innate immunity . . Among them, negative regulator of interferon response NRIR lncRNA NRIR, also known as lncRNA-CMPK2 is a non-coding ISG that negatively regulates IFITM1 and Mx1 expression in HCV infection . .", ". Notably, IFITM3 is largely homologous to IFITM1, but the role of NRIR in the regulation of IFITM3 in HTNV infection remains unclear. In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans.", "In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans. We provide primary evidence suggesting that IFITM3, regulated by NRIR, can inhibit HTNV infection and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection.", "This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection. This study was conducted in accordance with the recommendations of the biomedical research guidelines involving human participants established by the National Health and Family Planning Commission of China. The Institutional Ethics Committee of Tangdu Hospital approved this study.", "The Institutional Ethics Committee of Tangdu Hospital approved this study. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained.", "Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained. Sixty-nine HFRS patients admitted into the Department of Infectious Diseases, Tangdu Hospital between October 2014 and March 2016 were enrolled in this study. All patients were Han Chinese.", "All patients were Han Chinese. The diagnosis of HFRS was made based on typical symptoms and signs as well as positive IgM and IgG antibodies against HTNV in the serum assessed by enzyme linked immunosorbent assay ELISA in our department. The classification of HFRS severity and the exclusion criteria were described as follows .", "The classification of HFRS severity and the exclusion criteria were described as follows . : white blood cells WBC , platelets PLT , blood urea nitrogen BUN , serum creatinine Scr , and heteromorphic lymphocytes that were tested by the Department of Clinical Laboratory shown in Table 1 . According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described .", "According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described . : . mild patients were identified with mild renal failure without an obvious oliguric stage; .", "mild patients were identified with mild renal failure without an obvious oliguric stage; . moderate patients were those with obvious symptoms of uremia, effusion bulbar conjunctiva , hemorrhage skin and mucous membrane , and renal failure with a typical oliguric stage; . severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and .", "severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and . critical patients exhibited ≥1 of the following signs during the illness: refractory shock, visceral hemorrhage, heart failure, pulmonary edema, brain edema, severe secondary infection, and severe renal failure with oliguria urine output, 50-500 ml/day for >5 days, anuria urine output, <50 ml/day for >2 days, or a BUN level of >42.84 mmol/l. Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group.", "Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group. The exclusion criteria for this study were patients with: . any other kidney disease, . diabetes mellitus, . autoimmune disease, . hematological disease, .", "diabetes mellitus, . autoimmune disease, . hematological disease, . cardiovascular disease, . viral hepatitis types A, B, C, D, or E , or . any other liver disease. In addition, no patients received corticosteroids or other immunomodulatory drugs during the study period . .", ". Genomic DNA was extracted from the peripheral blood of patients using the PureGene DNA Isolation kit Gentra Systems, Minneapolis, MN, USA . The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ .", "The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ . The PCR products were purified and sequenced using an Applied Biosystems 3730xl DNA Analyzer Thermo Scientific, Waltham, MA, USA . The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods .", "The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods . . Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA .", "Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA . The SuperScript III Platinum One-Step Quantitative RT-PCR System kit Invitrogen, Carlsbad, CA, USA was employed for the real-time RT-PCR assay. The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′.", "The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′. The synthetic S segment of the HTNV standard strain 76-118 RNA transcript was used as the quantitative calibrator. The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators.", "The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators. For each experiment, one aliquot of calibrated 76-118 standard was extracted in parallel with the clinical samples and serially 10-fold diluted with concentrations ranging from 10.56 to 2.56 log10 copies/ml. PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C.", "PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C. Fluorescence was read during the 72°C step of the final segment of every cycling program. HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator.", "HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator. A549 cells ATCC Cat# CRM-CCL-185, RRID:CVCL_0023 were grown in our laboratory in DMEM with 10% FBS Thermo Scientific, Waltham, MA, USA in a 5% CO2 incubator. Cells were used within passage 10 after primary culture.", "Cells were used within passage 10 after primary culture. HTNV strain 76-118 was cultured in Vero E6 cells ATCC Cat# CRL-1586, RRID:CVCL_0574 in our laboratory and titrated using an immunofluorescence staining assay for HTNV nucleocapsid protein NP as previously described . . The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method.", ". The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method. The recombinant human IFN-α2a was obtained from PBL Interferon Source Piscataway, NJ, USA and dissolved in the buffer provided by the manufacturer composition not disclosed . HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins.", "HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins. Subsequently, the virus solution was removed and fresh medium added to the cell culture. Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10.", "Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10. Puromycin 2 μg/ ml for HUVEC and 6 μg/ml for A549 cells was used to create cell lines stably expressing IFITMs. Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA .", "Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA . SiRNAs were purchased from Origene Rockville, MD, USA , and the sequences were not disclosed. Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA .", "Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA . Quantitative realtime PCR qPCR was performed using SYBR Premix Ex Taq II Takara Biotechnology Co., Dalian, China with a Bio-Rad iQ5 cycler Bio-Rad, Hercules, CA, USA . β-actin was used as the reference gene.", "β-actin was used as the reference gene. The primers Sangon Biotech, Shanghai, China were as follows: IFITM1 forward, 5′-ACTCCGTGAAGTCTAGGGACA-3′ and reverse, 5′-TGTCACAGAGCCGAATACCAG-3′ ; IFITM2 forward, 5′-ATCCCGGTAACCCGATCAC-3′ and reverse, 5′-CTTCCTGTCCCTAGACTTCAC-3′ ; IFITM3 forward, 5′-GGTCTTCGCTGGACACCAT-3′ and reverse, 5′-TGTCCCTAGACTTCACGGAGTA-3′ ; IFITM3 pre-mRNA forward, 5′-CATAGCACGCGGCTCT CAG-3′ and reverse, 5′-CGTCGCCAACCATCTTCCTG-3′ ; HTNV S segment forward, 5′-GCCTGGAGACCATCTGA AAG-3′ and reverse, 5′-AGTATCGGGACGACAAAGGA-3′ ; β-actin forward, 5′-GCTACGTCGCCCTGGACTTC-3′ and reverse, 5′-GTCATAGTCCGCCTAGAAGC-3′ ; NRIR forward, 5′-ATGGTTTTCTGGTGCCTTG-3′ and reverse, 5′-GGAGGTTAGAGGTGTCTGCTG-3′ ; NRAV forward, 5′-TCACTACTGCCCCAGGATCA-3′ and reverse, 5′-GGTGGTCACAGGACTCATGG-3′ . For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA .", "For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . MiR-130a level was determined using the gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control .", "U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control . . Because the pre-mRNA levels can represent the initial transcription rate . , the primers used to detect the pre-mRNA of IFITM3 were designed targeting the intron of IFITM3 as previously described . . IFITM3 has two exons and one intron.", ". IFITM3 has two exons and one intron. For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon.", "For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon. For qPCR of IFITM3 mRNA, the forward primers were positioned in the first exon, and the reverse primer was positioned at the beginning of the second exon . .", ". Because the basal expression of IFITM3 is low in A549 cells, we detected IFITM3 mRNA and pre-mRNA in A549 cells following IFN-α2a treatment 20 IU/ml for 12 h after the overexpression of NRIR. Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA .", "Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA . Equal amounts of protein 20 μg protein/lane were electrophoresed on a 10%-SDS-polyacrylamide gel and electrophoretically transferred to a polyvinylidene difluoride membrane Millipore, Billerica, MA, USA . After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C.", "After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C. The membranes were then washed and incubated with HRP-conjugated IgG antibody Cell Signaling Technology, Danvers, MA, USA for 1 h at room temperature. The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film.", "The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film. The blot densities were analyzed using the Quantity One software Bio-Rad, Hercules, CA, USA . In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS.", "In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS. Protease inhibitor cocktail Roche, Basel, Switzerland was added before use. The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 .", "The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 . At the indicated times post-HTNV infection, the cells were fixed with 4% PFA, incubated with 0.3% Triton X-100 Sigma-Aldrich, St. Louis, MO, USA , and blocked with 5% BSA for 1 h. Following incubation with a mouse monoclonal antibody against c-myc-tag Sigma-Aldrich, St. Louis, MO, USA, Sigma-Aldrich Cat# M5546 , IFITM3, lysosome-associated membrane glycoprotein 1 LAMP1, Cell Signaling Technology, Danvers, MA, USA , or HTNV NP at 37°C for 2 h, the cells were washed and incubated with anti-rabbit Ig conjugated to Alexa 555 and anti-mouse Ig conjugated to Alexa 488 Abcam, Cambridge, MA, USA secondary antibodies at room temperature for 1 h. The nuclei were counterstained with DAPI. An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images.", "An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images. hTnV binding and entry assay Cells transduced with IFITM3 or the empty vector were detached and washed extensively with cold PBS. The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation.", "The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation. Part of cells were washed extensively with ice-cold PBS and harvested for binding assay. Another part of cells were switched to 37°C for 2 h to allow HTNV entry.", "Another part of cells were switched to 37°C for 2 h to allow HTNV entry. The HTNV that remained on the cell surface was removed by treatment with proteinase K 0.1 mg/ml, Thermo Scientific, Waltham, MA, USA . To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl.", "To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl. Adsorption of HTNV moi = 1 was performed at 4°C for 1 h. The cells were then washed, and fusion of the virus with the plasma membrane was triggered by incubation in low pH medium 20 mM sodium succinate, pH = 5.5 for 10 min at 37°C. Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl .", "Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl . . qPCR analysis of the HTNV S segment was conducted to evaluate the influence of IFITMs on HTNV cell adhesion and HTNV entry. All data were expressed as the mean ± SEM.", "All data were expressed as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 GraphPad Software, La Jolla, CA, USA . For association analysis of the rs12252 allele and genotype, Fisher's exact test was used. Independent samples t-tests were used for normally distributed data.", "Independent samples t-tests were used for normally distributed data. Differences among groups were determined by one-way analysis of variance ANOVA with repeated measures, followed by Bonferroni's post hoc test. P < 0.05 was considered statistically significant.", "P < 0.05 was considered statistically significant. The iFiTM3 snP rs12252 c allele and cc genotype associated with severe hFrs Disease and a higher Plasma hTnV load To determine the clinical significance of IFITM3 SNP in HTNV infection, the relationship between rs12252 SNP and the severity of HFRS in 69 patients were examined. We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients.", "We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients. Then, we stratified these patients into mild and severe, based on the clinical assessment as described in Section \"Material and Methods. \" We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 .", "We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 . The frequency of rs12252 C in severe patients was also higher than those mild patients 68.29 vs. 46.43%, P = 0.013, Figures 1A,B; Table 2 . These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 .", "These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 . For genotypes, 43.90% of the severe patients carried the CC genotype, a significantly higher frequency than the control Han Chinese per 1,000 genomes sequence database 26.92% CC genotype, P = 0.03 as well as mildly infected patients 14.29%, P = 0.02, Figures 1A,B ; Table 2 . However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity.", "However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity. c The plasma HTNV load in CC genotype patients and CT/TT genotype patients, tested by qRCR analysis. Each symbol represents one individual patient.", "Each symbol represents one individual patient. Independent samples t-test was used to test the difference of HTNV load between groups. *P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population.", "*P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population. In addition, we also found that patients with CC genotype had higher plasma viral load in acute phase Figure 1C . These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS.", "These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS. Previous studies reveal that the truncated IFITM3 protein produced by SNP rs12252 C allele Figure 2A , the missing part stands for the truncated 21 amino acids from N-terminal of IFITM3, the intramembrane helix, and transmembrane helix was presented as boxes leads to an impaired anti-influenza activity . .", ". To test the functional significance of this polymorphism in HTNV infection, we transfected the majority T or minority C variant IFITM3 alleles that produce full-length or N-terminally truncated NΔ21 proteins Figure 2A with c-myc-tag to HUVEC and A549 cell using lentivirus vectors Figure 2B . Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material .", "Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material . Indeed, compared with the mock empty vector -infected control, the NΔ21 protein almost lost the ability to inhibit HTNV infection in both HUVEC and A549 cells Figures 2C,D ; Figure S3 in Supplementary Material . To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material .", "To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material . While we observed only a moderate upregulation of IFITM1, 2, and 3 mRNA and protein in HUVECs after more than 24 h postinfection; IFITM1, 2, and 3 mRNA, however, were only transiently upregulated in A549 cells and caused no significant change in protein level. We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually.", "We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually. The effect of siRNAs on the expression of target IFITMs was tested by qPCR in HUVECs Figure S2 in Supplementary Material , and the effect of the best oligo against each IFITMs IFITM1C, IFITM2A, IFITM3B was tested by Western blot in A549 Figure 4A and HUVEC cells Figure 4B . To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h .", "To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h . The cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were significantly suppressed in both HUVEC and A549 cells in response to IFN-α2a treatment. Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells.", "Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells. Knockdown of IFITM1 also partially restored the HTNV level in A549 cells Figures 4C,D . These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection.", "These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection. c-myc-tagged IFITM1, 2, and 3 were expressed in both HUVEC and A549 cells Figure 5A , and the cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were suppressed by IFITM3 overexpression in HUVEC cells Figures 5B-D . They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D .", "They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D . The inhibitory effect of IFITM3 was further confirmed by immunofluorescence analysis of HTNV NP Figure S3 in Supplementary Material . These results were in accordance with the above-described RNAi results.", "These results were in accordance with the above-described RNAi results. To determine whether IFITM3 inhibited HTNV binding or entry, HUVEC and A549 cells were incubated with HTNV moi = 1 at 4°C for 1 h, unbound virus was washed away, and HTNV RNA collected at this time point represents HTNV bound to the cell surface. After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus.", "After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus. As a positive control for inhibition of virus entry, we incubated a parallel group of cells with HTNV at pH = 5.5 as described in Section \"Materials and Methods.\" Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B .", "Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B . iFiTM3 Was Partially localized to laMP1 + late endosomes in the host cells To elucidate the mechanism of IFITM3 function, we investigated the subcellular localization of IFTIM3 in the host cells. IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C .", "IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C . The co-localization of IFITM3 and LAMP1 + late endosomes had also been found in A549 cells . . Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry .", "Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry . , this result provides an evidence for the anti-HTNV mechanism of IFITM3. LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies.", "LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies. We tested the change of previously reported regulators of IFITMs, such as NRAV, NRIR, and miR-130a after HTNV infection, among which NRIR was the only changed one downregulated after HTNV infection Figure 7A ; Figure S4 in Supplementary Material in HUVEC. However, the expression of NRIR was unchanged in A549 cells.", "However, the expression of NRIR was unchanged in A549 cells. We overexpressed NRIR in HUVEC and A549 cells using the pcDNA3.1 vector Figure 7B . Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E .", "Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E . These data suggest that lncRNA NRIR is a negative regulator of IFITM3 transcription. Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae.", "Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae. It causes HFRS, which is an important threat to public health worldwide. It is also a potential weapon for biological terrorism. Reservoir animals, usually rodents, are asymptomatic during persistent infection.", "Reservoir animals, usually rodents, are asymptomatic during persistent infection. Unlike in rodents, Hantavirus infection leads to HFRS and Hantavirus pulmonary syndrome HPS in humans . . The major clinical characteristics of HFRS include fever, hemorrhage, hypotension, and renal injury . , causing severe manifestations and death in some cases.", ", causing severe manifestations and death in some cases. The current standard of care for HFRS relies on symptomatic and supportive treatment. It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS ..", "It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS .. However, no direct antiviral medications are currently available for this illness. Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases.", "Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases. It has been reported that HTNV infection induces a late type I interferon response . . However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified.", "However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified. Therefore, identification of ISGs that are effective against HTNV is an attractive strategy to identify novel therapeutic targets. In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese.", "In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese. The rs12252 C allele and CC genotype are also found to be associated with higher plasma viral load in the early stage of HFRS. We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity.", "We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity. Interestingly, IFITM3 is found to restrict HTNV infection with a mechanism of cellular entry inhibition. Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry.", "Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry. In addition, we find that HTNV infection downregulated lncRNA NRIR 48 h post infection, which negatively regulates the transcription of IFITM3. Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS.", "Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. The antiviral properties of IFITM proteins were identified in 2009 in an RNAi screen for host factors that influence influenza virus replication . .", ". IFITM1, 2, and 3 have been demonstrated to possess antiviral activity in several studies. Everitt et al. demonstrated that the severity of influenza virus infection was greatly increased in IFITM3-knockout mice compared with wild-type animals . .", ". Different IFITM members have also been confirmed to inhibit the cellular entry of multiple virus families including filoviruses, rhabdoviruses, and flaviviruses 7, . . . 30 . For example, HIV-1 and HCV infection are inhibited by IFITM1 . . . . . It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry .", "It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry . . Recent studies suggested that the cellular location of different IFITMs may influence the range of viruses restricted by each protein . . IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles .", "IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles . , whereas IFITM3 confines influenza virus in acidified endosomal compartments . .", ", whereas IFITM3 confines influenza virus in acidified endosomal compartments . . Notably, retrovirus subvirus particles ISVPs , which do not require endosomal acidification for entry, are not inhibited by IFITM3 expression, suggesting that IFITM3 may function at the stage of endosomal entry . .", ". Studies utilizing cell-cell fusion assays have suggested that IFITM3 blocks the entry of enveloped virus by preventing the fusion of the viral membrane with a limiting membrane of the host cell, either the plasma membrane and/or the endosomal membranes. The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes .", "The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes . . In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells.", "In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells. Overexpression of IFITM3 inhibited HTNV infection to HUVEC and A549 cells. IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells.", "IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells. All these results suggest that IFITM3 is an important control factor under natural infection of HTNV. Our results also demonstrate that the effectiveness of IFITM3 is cell type-independent, which is in accordance with the results from similar viruses, such as RVFV . .", ". Binding and entry assays, conducted by controlling the temperature and pH, showed that IFITM3 did not significantly influence HTNV binding but inhibited HTNV entry into HUVEC and A549 cells. Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry.", "Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry. However, we failed in tracking the transportation of HTNV in infected cells possibly due to the lack of fluorescence-labeled virus. In addition, IFITM1 also suppressed HTNV infection in A549 cells.", "In addition, IFITM1 also suppressed HTNV infection in A549 cells. The mechanism underlying anti-HTNV effect of IFITM1 remains undetermined and deserves to be further explored. According to a recent study on the three-dimensional structure of IFITM3, there is a C-terminal transmembrane α-helix and a two-N-terminal intramembrane α-helices shown in Figure 2A as black boxes in IFITM3 . .", ". There are two splice variants that differ by the presence or absence of the first N-terminal 21 amino acids deleted part, shown in Figure 2A as red dotted line . Several SNPs including 13 non-synonymous, 13 synonymous, 1 in-frame stop, and 1 splice site acceptoraltering have been reported in the translated IFITM3 sequence . .", ". Among them, the rare SNP rs12252C allele of IFITM3 truncates the protein as described above, leading to a reduced inhibition of influenza virus infection in A549 cells . . We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro.", "We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro. In Northern European patients hospitalized with seasonal influenza or pandemic influenza A virus, increased homozygosity of the minor C allele of SNP rs12252 in IFITM3 was observed . .", ". In Chinese patients infected with influenza A H1N1 virus, there was also an increased frequency of the C allele and CC genotype of SNP rs12252 . . In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients.", "In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients. Patients carrying the CC genotype also had higher plasma viral loads compared with those with the CT/TT genotype. Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo.", "Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo. We speculate that the much higher level of CC allele at healthy population of Han Chinese compared with Caucasians may place the Chinese at a higher risk for developing severe illness upon HTNV infection, which needs further investigation. LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity.", "LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity. For example, lncRNA NEAT1 is reported to be upregulated by influenza virus or PolyI:C stimulation, which promotes IL-8 expression . .", ". lncRNA NRAV has been shown to negatively regulate the initial transcription of IFITM3 and Mx1 by affecting the histone modification of these genes . . lncRNA NRIR is a non-coding ISG, which has been reported to negatively regulate IFITM1 and Mx1 expression in HCV infection . . Mir-130a was also reported as a regulator of IFITM1 . .", ". Mir-130a was also reported as a regulator of IFITM1 . . In this analysis, lncRNA NRIR was downregulated in HUVECs after HTNV infection for 48 h, overexpression of NRIR negatively regulates the initial transcription of IFITM3, evidenced by the decreased pre-mRNA as well as mRNA levels. NRIR overexpression also facilitated HTNV infection.", "NRIR overexpression also facilitated HTNV infection. These results indicate that the downregulation of NRIR after HTNV infection is possibly involved in the activation of innate immune responses against HTNV infection. We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study.", "We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study. Another lncRNA that can regulate IFITM3, i.e., NRAV NR_038854 , remained unchanged after HTNV infection Figures S4A,B in Supplementary Material . Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material .", "Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material . In conclusion, this study revealed a critical role for IFITM3 in HTNV infection. We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells.", "We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells. In addition, we discovered that the IFITM3 SNP rs12252 C allele and CC genotype correlates with the plasma HTNV load and the severity of HFRS; and the rs12252 C allele produces a truncated IFITM3 protein NΔ21 that attenuates its anti-HTNV function. These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease.", "These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease. aUThOr cOnTribUTiOns ZX-y, BP-y, YC-t, and MH-w performed the experiments; WP-z, BX-f, LY-f, ZY, and JZ-s designed the research; HC-x, YW, and WX analyzed the data; TK and ZC-m provided clinical data; ZX-y and BP-y wrote the paper." ]

[ "Hantaan virus HTNV causes hemorrhagic fever with renal syndrome HFRS . Previous studies have identified interferon-induced transmembrane proteins IFITMs as an interferon-stimulated gene family. However, the role of IFITMs in HTNV infection is unclear.", "However, the role of IFITMs in HTNV infection is unclear. In this study, we observed that IFITM3 single nucleotide polymorphisms SNP rs12252 C allele and CC genotype associated with the disease severity and HTNV load in the plasma of HFRS patients. In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity.", "In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity. We also proved that IFITM3 was able to inhibit HTNV infection in both HUVEC and A549 cells by overexpression and RNAi assays, likely via a mechanism of inhibiting virus entry demonstrated by binding and entry assay. Localization of IFITM3 in late endosomes was also observed.", "Localization of IFITM3 in late endosomes was also observed. In addition, we demonstrated that the transcription of IFITM3 is negatively regulated by an lncRNA negative regulator of interferon response NRIR . Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS.", "Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS. Text: associates with the severity of disease, indicating the importance of viremia in the pathogenesis of HFRS . .", ". Therefore, further studies of host factors limiting HTNV infection and influencing antiviral response as well as disease progression are clinically significant and timely. The human family of interferon-induced transmembrane proteins IFITMs was discovered 25 years ago to consist of interferon-stimulated genes ISGs . .", ". This family includes five members, namely, IFITM1, 2, 3, 5, and 10, among which IFITM1, 2, and 3 possess antiviral activity . . Different IFITM proteins have different antiviral spectrum . . For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice .", "For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice . , and it also inhibits multiple viruses, including filoviruses, rhabdoviruses, flaviviruses, and even Ebola and Zika virus . . . . . . The antiviral mechanism of IFITM3 is thought to be the restriction of viral entry into cells . .", ". Single nucleotide polymorphisms SNPs are single nucleotide variations in a genetic sequence that occur at an appreciable frequency in the population. Several SNPs has been identified in IFITM3, among which the rs12252 site with C allele results in a N-terminal truncation of IFITM3 protein, leading to impaired inhibition of influenza virus in vitro . .", ". Notably, the frequencies of rs12252 C allele and CC genotype correlate with disease severity in patients infected with influenza virus . . HTNV has been shown to induce a type I interferon response though in later time postinfection . . While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection .", "While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection . , however, the effect of IFITMs on HTNV infection in human cell lines and its role in HFRS still remain unknown. LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators.", "LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators. Some lncRNAs have been shown to play a role in innate immunity . . Among them, negative regulator of interferon response NRIR lncRNA NRIR, also known as lncRNA-CMPK2 is a non-coding ISG that negatively regulates IFITM1 and Mx1 expression in HCV infection . .", ". Notably, IFITM3 is largely homologous to IFITM1, but the role of NRIR in the regulation of IFITM3 in HTNV infection remains unclear. In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans.", "In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans. We provide primary evidence suggesting that IFITM3, regulated by NRIR, can inhibit HTNV infection and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection.", "This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection. This study was conducted in accordance with the recommendations of the biomedical research guidelines involving human participants established by the National Health and Family Planning Commission of China. The Institutional Ethics Committee of Tangdu Hospital approved this study.", "The Institutional Ethics Committee of Tangdu Hospital approved this study. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained.", "Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained. Sixty-nine HFRS patients admitted into the Department of Infectious Diseases, Tangdu Hospital between October 2014 and March 2016 were enrolled in this study. All patients were Han Chinese.", "All patients were Han Chinese. The diagnosis of HFRS was made based on typical symptoms and signs as well as positive IgM and IgG antibodies against HTNV in the serum assessed by enzyme linked immunosorbent assay ELISA in our department. The classification of HFRS severity and the exclusion criteria were described as follows .", "The classification of HFRS severity and the exclusion criteria were described as follows . : white blood cells WBC , platelets PLT , blood urea nitrogen BUN , serum creatinine Scr , and heteromorphic lymphocytes that were tested by the Department of Clinical Laboratory shown in Table 1 . According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described .", "According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described . : . mild patients were identified with mild renal failure without an obvious oliguric stage; .", "mild patients were identified with mild renal failure without an obvious oliguric stage; . moderate patients were those with obvious symptoms of uremia, effusion bulbar conjunctiva , hemorrhage skin and mucous membrane , and renal failure with a typical oliguric stage; . severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and .", "severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and . critical patients exhibited ≥1 of the following signs during the illness: refractory shock, visceral hemorrhage, heart failure, pulmonary edema, brain edema, severe secondary infection, and severe renal failure with oliguria urine output, 50-500 ml/day for >5 days, anuria urine output, <50 ml/day for >2 days, or a BUN level of >42.84 mmol/l. Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group.", "Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group. The exclusion criteria for this study were patients with: . any other kidney disease, . diabetes mellitus, . autoimmune disease, . hematological disease, .", "diabetes mellitus, . autoimmune disease, . hematological disease, . cardiovascular disease, . viral hepatitis types A, B, C, D, or E , or . any other liver disease. In addition, no patients received corticosteroids or other immunomodulatory drugs during the study period . .", ". Genomic DNA was extracted from the peripheral blood of patients using the PureGene DNA Isolation kit Gentra Systems, Minneapolis, MN, USA . The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ .", "The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ . The PCR products were purified and sequenced using an Applied Biosystems 3730xl DNA Analyzer Thermo Scientific, Waltham, MA, USA . The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods .", "The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods . . Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA .", "Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA . The SuperScript III Platinum One-Step Quantitative RT-PCR System kit Invitrogen, Carlsbad, CA, USA was employed for the real-time RT-PCR assay. The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′.", "The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′. The synthetic S segment of the HTNV standard strain 76-118 RNA transcript was used as the quantitative calibrator. The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators.", "The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators. For each experiment, one aliquot of calibrated 76-118 standard was extracted in parallel with the clinical samples and serially 10-fold diluted with concentrations ranging from 10.56 to 2.56 log10 copies/ml. PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C.", "PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C. Fluorescence was read during the 72°C step of the final segment of every cycling program. HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator.", "HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator. A549 cells ATCC Cat# CRM-CCL-185, RRID:CVCL_0023 were grown in our laboratory in DMEM with 10% FBS Thermo Scientific, Waltham, MA, USA in a 5% CO2 incubator. Cells were used within passage 10 after primary culture.", "Cells were used within passage 10 after primary culture. HTNV strain 76-118 was cultured in Vero E6 cells ATCC Cat# CRL-1586, RRID:CVCL_0574 in our laboratory and titrated using an immunofluorescence staining assay for HTNV nucleocapsid protein NP as previously described . . The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method.", ". The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method. The recombinant human IFN-α2a was obtained from PBL Interferon Source Piscataway, NJ, USA and dissolved in the buffer provided by the manufacturer composition not disclosed . HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins.", "HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins. Subsequently, the virus solution was removed and fresh medium added to the cell culture. Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10.", "Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10. Puromycin 2 μg/ ml for HUVEC and 6 μg/ml for A549 cells was used to create cell lines stably expressing IFITMs. Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA .", "Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA . SiRNAs were purchased from Origene Rockville, MD, USA , and the sequences were not disclosed. Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA .", "Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA . Quantitative realtime PCR qPCR was performed using SYBR Premix Ex Taq II Takara Biotechnology Co., Dalian, China with a Bio-Rad iQ5 cycler Bio-Rad, Hercules, CA, USA . β-actin was used as the reference gene.", "β-actin was used as the reference gene. The primers Sangon Biotech, Shanghai, China were as follows: IFITM1 forward, 5′-ACTCCGTGAAGTCTAGGGACA-3′ and reverse, 5′-TGTCACAGAGCCGAATACCAG-3′ ; IFITM2 forward, 5′-ATCCCGGTAACCCGATCAC-3′ and reverse, 5′-CTTCCTGTCCCTAGACTTCAC-3′ ; IFITM3 forward, 5′-GGTCTTCGCTGGACACCAT-3′ and reverse, 5′-TGTCCCTAGACTTCACGGAGTA-3′ ; IFITM3 pre-mRNA forward, 5′-CATAGCACGCGGCTCT CAG-3′ and reverse, 5′-CGTCGCCAACCATCTTCCTG-3′ ; HTNV S segment forward, 5′-GCCTGGAGACCATCTGA AAG-3′ and reverse, 5′-AGTATCGGGACGACAAAGGA-3′ ; β-actin forward, 5′-GCTACGTCGCCCTGGACTTC-3′ and reverse, 5′-GTCATAGTCCGCCTAGAAGC-3′ ; NRIR forward, 5′-ATGGTTTTCTGGTGCCTTG-3′ and reverse, 5′-GGAGGTTAGAGGTGTCTGCTG-3′ ; NRAV forward, 5′-TCACTACTGCCCCAGGATCA-3′ and reverse, 5′-GGTGGTCACAGGACTCATGG-3′ . For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA .", "For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . MiR-130a level was determined using the gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control .", "U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control . . Because the pre-mRNA levels can represent the initial transcription rate . , the primers used to detect the pre-mRNA of IFITM3 were designed targeting the intron of IFITM3 as previously described . . IFITM3 has two exons and one intron.", ". IFITM3 has two exons and one intron. For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon.", "For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon. For qPCR of IFITM3 mRNA, the forward primers were positioned in the first exon, and the reverse primer was positioned at the beginning of the second exon . .", ". Because the basal expression of IFITM3 is low in A549 cells, we detected IFITM3 mRNA and pre-mRNA in A549 cells following IFN-α2a treatment 20 IU/ml for 12 h after the overexpression of NRIR. Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA .", "Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA . Equal amounts of protein 20 μg protein/lane were electrophoresed on a 10%-SDS-polyacrylamide gel and electrophoretically transferred to a polyvinylidene difluoride membrane Millipore, Billerica, MA, USA . After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C.", "After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C. The membranes were then washed and incubated with HRP-conjugated IgG antibody Cell Signaling Technology, Danvers, MA, USA for 1 h at room temperature. The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film.", "The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film. The blot densities were analyzed using the Quantity One software Bio-Rad, Hercules, CA, USA . In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS.", "In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS. Protease inhibitor cocktail Roche, Basel, Switzerland was added before use. The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 .", "The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 . At the indicated times post-HTNV infection, the cells were fixed with 4% PFA, incubated with 0.3% Triton X-100 Sigma-Aldrich, St. Louis, MO, USA , and blocked with 5% BSA for 1 h. Following incubation with a mouse monoclonal antibody against c-myc-tag Sigma-Aldrich, St. Louis, MO, USA, Sigma-Aldrich Cat# M5546 , IFITM3, lysosome-associated membrane glycoprotein 1 LAMP1, Cell Signaling Technology, Danvers, MA, USA , or HTNV NP at 37°C for 2 h, the cells were washed and incubated with anti-rabbit Ig conjugated to Alexa 555 and anti-mouse Ig conjugated to Alexa 488 Abcam, Cambridge, MA, USA secondary antibodies at room temperature for 1 h. The nuclei were counterstained with DAPI. An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images.", "An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images. hTnV binding and entry assay Cells transduced with IFITM3 or the empty vector were detached and washed extensively with cold PBS. The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation.", "The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation. Part of cells were washed extensively with ice-cold PBS and harvested for binding assay. Another part of cells were switched to 37°C for 2 h to allow HTNV entry.", "Another part of cells were switched to 37°C for 2 h to allow HTNV entry. The HTNV that remained on the cell surface was removed by treatment with proteinase K 0.1 mg/ml, Thermo Scientific, Waltham, MA, USA . To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl.", "To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl. Adsorption of HTNV moi = 1 was performed at 4°C for 1 h. The cells were then washed, and fusion of the virus with the plasma membrane was triggered by incubation in low pH medium 20 mM sodium succinate, pH = 5.5 for 10 min at 37°C. Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl .", "Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl . . qPCR analysis of the HTNV S segment was conducted to evaluate the influence of IFITMs on HTNV cell adhesion and HTNV entry. All data were expressed as the mean ± SEM.", "All data were expressed as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 GraphPad Software, La Jolla, CA, USA . For association analysis of the rs12252 allele and genotype, Fisher's exact test was used. Independent samples t-tests were used for normally distributed data.", "Independent samples t-tests were used for normally distributed data. Differences among groups were determined by one-way analysis of variance ANOVA with repeated measures, followed by Bonferroni's post hoc test. P < 0.05 was considered statistically significant.", "P < 0.05 was considered statistically significant. The iFiTM3 snP rs12252 c allele and cc genotype associated with severe hFrs Disease and a higher Plasma hTnV load To determine the clinical significance of IFITM3 SNP in HTNV infection, the relationship between rs12252 SNP and the severity of HFRS in 69 patients were examined. We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients.", "We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients. Then, we stratified these patients into mild and severe, based on the clinical assessment as described in Section \"Material and Methods. \" We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 .", "We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 . The frequency of rs12252 C in severe patients was also higher than those mild patients 68.29 vs. 46.43%, P = 0.013, Figures 1A,B; Table 2 . These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 .", "These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 . For genotypes, 43.90% of the severe patients carried the CC genotype, a significantly higher frequency than the control Han Chinese per 1,000 genomes sequence database 26.92% CC genotype, P = 0.03 as well as mildly infected patients 14.29%, P = 0.02, Figures 1A,B ; Table 2 . However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity.", "However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity. c The plasma HTNV load in CC genotype patients and CT/TT genotype patients, tested by qRCR analysis. Each symbol represents one individual patient.", "Each symbol represents one individual patient. Independent samples t-test was used to test the difference of HTNV load between groups. *P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population.", "*P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population. In addition, we also found that patients with CC genotype had higher plasma viral load in acute phase Figure 1C . These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS.", "These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS. Previous studies reveal that the truncated IFITM3 protein produced by SNP rs12252 C allele Figure 2A , the missing part stands for the truncated 21 amino acids from N-terminal of IFITM3, the intramembrane helix, and transmembrane helix was presented as boxes leads to an impaired anti-influenza activity . .", ". To test the functional significance of this polymorphism in HTNV infection, we transfected the majority T or minority C variant IFITM3 alleles that produce full-length or N-terminally truncated NΔ21 proteins Figure 2A with c-myc-tag to HUVEC and A549 cell using lentivirus vectors Figure 2B . Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material .", "Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material . Indeed, compared with the mock empty vector -infected control, the NΔ21 protein almost lost the ability to inhibit HTNV infection in both HUVEC and A549 cells Figures 2C,D ; Figure S3 in Supplementary Material . To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material .", "To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material . While we observed only a moderate upregulation of IFITM1, 2, and 3 mRNA and protein in HUVECs after more than 24 h postinfection; IFITM1, 2, and 3 mRNA, however, were only transiently upregulated in A549 cells and caused no significant change in protein level. We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually.", "We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually. The effect of siRNAs on the expression of target IFITMs was tested by qPCR in HUVECs Figure S2 in Supplementary Material , and the effect of the best oligo against each IFITMs IFITM1C, IFITM2A, IFITM3B was tested by Western blot in A549 Figure 4A and HUVEC cells Figure 4B . To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h .", "To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h . The cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were significantly suppressed in both HUVEC and A549 cells in response to IFN-α2a treatment. Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells.", "Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells. Knockdown of IFITM1 also partially restored the HTNV level in A549 cells Figures 4C,D . These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection.", "These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection. c-myc-tagged IFITM1, 2, and 3 were expressed in both HUVEC and A549 cells Figure 5A , and the cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were suppressed by IFITM3 overexpression in HUVEC cells Figures 5B-D . They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D .", "They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D . The inhibitory effect of IFITM3 was further confirmed by immunofluorescence analysis of HTNV NP Figure S3 in Supplementary Material . These results were in accordance with the above-described RNAi results.", "These results were in accordance with the above-described RNAi results. To determine whether IFITM3 inhibited HTNV binding or entry, HUVEC and A549 cells were incubated with HTNV moi = 1 at 4°C for 1 h, unbound virus was washed away, and HTNV RNA collected at this time point represents HTNV bound to the cell surface. After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus.", "After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus. As a positive control for inhibition of virus entry, we incubated a parallel group of cells with HTNV at pH = 5.5 as described in Section \"Materials and Methods.\" Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B .", "Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B . iFiTM3 Was Partially localized to laMP1 + late endosomes in the host cells To elucidate the mechanism of IFITM3 function, we investigated the subcellular localization of IFTIM3 in the host cells. IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C .", "IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C . The co-localization of IFITM3 and LAMP1 + late endosomes had also been found in A549 cells . . Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry .", "Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry . , this result provides an evidence for the anti-HTNV mechanism of IFITM3. LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies.", "LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies. We tested the change of previously reported regulators of IFITMs, such as NRAV, NRIR, and miR-130a after HTNV infection, among which NRIR was the only changed one downregulated after HTNV infection Figure 7A ; Figure S4 in Supplementary Material in HUVEC. However, the expression of NRIR was unchanged in A549 cells.", "However, the expression of NRIR was unchanged in A549 cells. We overexpressed NRIR in HUVEC and A549 cells using the pcDNA3.1 vector Figure 7B . Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E .", "Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E . These data suggest that lncRNA NRIR is a negative regulator of IFITM3 transcription. Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae.", "Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae. It causes HFRS, which is an important threat to public health worldwide. It is also a potential weapon for biological terrorism. Reservoir animals, usually rodents, are asymptomatic during persistent infection.", "Reservoir animals, usually rodents, are asymptomatic during persistent infection. Unlike in rodents, Hantavirus infection leads to HFRS and Hantavirus pulmonary syndrome HPS in humans . . The major clinical characteristics of HFRS include fever, hemorrhage, hypotension, and renal injury . , causing severe manifestations and death in some cases.", ", causing severe manifestations and death in some cases. The current standard of care for HFRS relies on symptomatic and supportive treatment. It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS ..", "It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS .. However, no direct antiviral medications are currently available for this illness. Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases.", "Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases. It has been reported that HTNV infection induces a late type I interferon response . . However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified.", "However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified. Therefore, identification of ISGs that are effective against HTNV is an attractive strategy to identify novel therapeutic targets. In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese.", "In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese. The rs12252 C allele and CC genotype are also found to be associated with higher plasma viral load in the early stage of HFRS. We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity.", "We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity. Interestingly, IFITM3 is found to restrict HTNV infection with a mechanism of cellular entry inhibition. Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry.", "Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry. In addition, we find that HTNV infection downregulated lncRNA NRIR 48 h post infection, which negatively regulates the transcription of IFITM3. Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS.", "Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. The antiviral properties of IFITM proteins were identified in 2009 in an RNAi screen for host factors that influence influenza virus replication . .", ". IFITM1, 2, and 3 have been demonstrated to possess antiviral activity in several studies. Everitt et al. demonstrated that the severity of influenza virus infection was greatly increased in IFITM3-knockout mice compared with wild-type animals . .", ". Different IFITM members have also been confirmed to inhibit the cellular entry of multiple virus families including filoviruses, rhabdoviruses, and flaviviruses 7, . . . 30 . For example, HIV-1 and HCV infection are inhibited by IFITM1 . . . . . It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry .", "It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry . . Recent studies suggested that the cellular location of different IFITMs may influence the range of viruses restricted by each protein . . IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles .", "IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles . , whereas IFITM3 confines influenza virus in acidified endosomal compartments . .", ", whereas IFITM3 confines influenza virus in acidified endosomal compartments . . Notably, retrovirus subvirus particles ISVPs , which do not require endosomal acidification for entry, are not inhibited by IFITM3 expression, suggesting that IFITM3 may function at the stage of endosomal entry . .", ". Studies utilizing cell-cell fusion assays have suggested that IFITM3 blocks the entry of enveloped virus by preventing the fusion of the viral membrane with a limiting membrane of the host cell, either the plasma membrane and/or the endosomal membranes. The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes .", "The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes . . In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells.", "In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells. Overexpression of IFITM3 inhibited HTNV infection to HUVEC and A549 cells. IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells.", "IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells. All these results suggest that IFITM3 is an important control factor under natural infection of HTNV. Our results also demonstrate that the effectiveness of IFITM3 is cell type-independent, which is in accordance with the results from similar viruses, such as RVFV . .", ". Binding and entry assays, conducted by controlling the temperature and pH, showed that IFITM3 did not significantly influence HTNV binding but inhibited HTNV entry into HUVEC and A549 cells. Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry.", "Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry. However, we failed in tracking the transportation of HTNV in infected cells possibly due to the lack of fluorescence-labeled virus. In addition, IFITM1 also suppressed HTNV infection in A549 cells.", "In addition, IFITM1 also suppressed HTNV infection in A549 cells. The mechanism underlying anti-HTNV effect of IFITM1 remains undetermined and deserves to be further explored. According to a recent study on the three-dimensional structure of IFITM3, there is a C-terminal transmembrane α-helix and a two-N-terminal intramembrane α-helices shown in Figure 2A as black boxes in IFITM3 . .", ". There are two splice variants that differ by the presence or absence of the first N-terminal 21 amino acids deleted part, shown in Figure 2A as red dotted line . Several SNPs including 13 non-synonymous, 13 synonymous, 1 in-frame stop, and 1 splice site acceptoraltering have been reported in the translated IFITM3 sequence . .", ". Among them, the rare SNP rs12252C allele of IFITM3 truncates the protein as described above, leading to a reduced inhibition of influenza virus infection in A549 cells . . We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro.", "We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro. In Northern European patients hospitalized with seasonal influenza or pandemic influenza A virus, increased homozygosity of the minor C allele of SNP rs12252 in IFITM3 was observed . .", ". In Chinese patients infected with influenza A H1N1 virus, there was also an increased frequency of the C allele and CC genotype of SNP rs12252 . . In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients.", "In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients. Patients carrying the CC genotype also had higher plasma viral loads compared with those with the CT/TT genotype. Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo.", "Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo. We speculate that the much higher level of CC allele at healthy population of Han Chinese compared with Caucasians may place the Chinese at a higher risk for developing severe illness upon HTNV infection, which needs further investigation. LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity.", "LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity. For example, lncRNA NEAT1 is reported to be upregulated by influenza virus or PolyI:C stimulation, which promotes IL-8 expression . .", ". lncRNA NRAV has been shown to negatively regulate the initial transcription of IFITM3 and Mx1 by affecting the histone modification of these genes . . lncRNA NRIR is a non-coding ISG, which has been reported to negatively regulate IFITM1 and Mx1 expression in HCV infection . . Mir-130a was also reported as a regulator of IFITM1 . .", ". Mir-130a was also reported as a regulator of IFITM1 . . In this analysis, lncRNA NRIR was downregulated in HUVECs after HTNV infection for 48 h, overexpression of NRIR negatively regulates the initial transcription of IFITM3, evidenced by the decreased pre-mRNA as well as mRNA levels. NRIR overexpression also facilitated HTNV infection.", "NRIR overexpression also facilitated HTNV infection. These results indicate that the downregulation of NRIR after HTNV infection is possibly involved in the activation of innate immune responses against HTNV infection. We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study.", "We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study. Another lncRNA that can regulate IFITM3, i.e., NRAV NR_038854 , remained unchanged after HTNV infection Figures S4A,B in Supplementary Material . Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material .", "Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material . In conclusion, this study revealed a critical role for IFITM3 in HTNV infection. We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells.", "We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells. In addition, we discovered that the IFITM3 SNP rs12252 C allele and CC genotype correlates with the plasma HTNV load and the severity of HFRS; and the rs12252 C allele produces a truncated IFITM3 protein NΔ21 that attenuates its anti-HTNV function. These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease.", "These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease. aUThOr cOnTribUTiOns ZX-y, BP-y, YC-t, and MH-w performed the experiments; WP-z, BX-f, LY-f, ZY, and JZ-s designed the research; HC-x, YW, and WX analyzed the data; TK and ZC-m provided clinical data; ZX-y and BP-y wrote the paper." ]

[ "Hantaan virus HTNV causes hemorrhagic fever with renal syndrome HFRS . Previous studies have identified interferon-induced transmembrane proteins IFITMs as an interferon-stimulated gene family. However, the role of IFITMs in HTNV infection is unclear.", "However, the role of IFITMs in HTNV infection is unclear. In this study, we observed that IFITM3 single nucleotide polymorphisms SNP rs12252 C allele and CC genotype associated with the disease severity and HTNV load in the plasma of HFRS patients. In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity.", "In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity. We also proved that IFITM3 was able to inhibit HTNV infection in both HUVEC and A549 cells by overexpression and RNAi assays, likely via a mechanism of inhibiting virus entry demonstrated by binding and entry assay. Localization of IFITM3 in late endosomes was also observed.", "Localization of IFITM3 in late endosomes was also observed. In addition, we demonstrated that the transcription of IFITM3 is negatively regulated by an lncRNA negative regulator of interferon response NRIR . Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS.", "Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS. Text: associates with the severity of disease, indicating the importance of viremia in the pathogenesis of HFRS . .", ". Therefore, further studies of host factors limiting HTNV infection and influencing antiviral response as well as disease progression are clinically significant and timely. The human family of interferon-induced transmembrane proteins IFITMs was discovered 25 years ago to consist of interferon-stimulated genes ISGs . .", ". This family includes five members, namely, IFITM1, 2, 3, 5, and 10, among which IFITM1, 2, and 3 possess antiviral activity . . Different IFITM proteins have different antiviral spectrum . . For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice .", "For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice . , and it also inhibits multiple viruses, including filoviruses, rhabdoviruses, flaviviruses, and even Ebola and Zika virus . . . . . . The antiviral mechanism of IFITM3 is thought to be the restriction of viral entry into cells . .", ". Single nucleotide polymorphisms SNPs are single nucleotide variations in a genetic sequence that occur at an appreciable frequency in the population. Several SNPs has been identified in IFITM3, among which the rs12252 site with C allele results in a N-terminal truncation of IFITM3 protein, leading to impaired inhibition of influenza virus in vitro . .", ". Notably, the frequencies of rs12252 C allele and CC genotype correlate with disease severity in patients infected with influenza virus . . HTNV has been shown to induce a type I interferon response though in later time postinfection . . While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection .", "While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection . , however, the effect of IFITMs on HTNV infection in human cell lines and its role in HFRS still remain unknown. LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators.", "LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators. Some lncRNAs have been shown to play a role in innate immunity . . Among them, negative regulator of interferon response NRIR lncRNA NRIR, also known as lncRNA-CMPK2 is a non-coding ISG that negatively regulates IFITM1 and Mx1 expression in HCV infection . .", ". Notably, IFITM3 is largely homologous to IFITM1, but the role of NRIR in the regulation of IFITM3 in HTNV infection remains unclear. In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans.", "In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans. We provide primary evidence suggesting that IFITM3, regulated by NRIR, can inhibit HTNV infection and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection.", "This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection. This study was conducted in accordance with the recommendations of the biomedical research guidelines involving human participants established by the National Health and Family Planning Commission of China. The Institutional Ethics Committee of Tangdu Hospital approved this study.", "The Institutional Ethics Committee of Tangdu Hospital approved this study. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained.", "Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained. Sixty-nine HFRS patients admitted into the Department of Infectious Diseases, Tangdu Hospital between October 2014 and March 2016 were enrolled in this study. All patients were Han Chinese.", "All patients were Han Chinese. The diagnosis of HFRS was made based on typical symptoms and signs as well as positive IgM and IgG antibodies against HTNV in the serum assessed by enzyme linked immunosorbent assay ELISA in our department. The classification of HFRS severity and the exclusion criteria were described as follows .", "The classification of HFRS severity and the exclusion criteria were described as follows . : white blood cells WBC , platelets PLT , blood urea nitrogen BUN , serum creatinine Scr , and heteromorphic lymphocytes that were tested by the Department of Clinical Laboratory shown in Table 1 . According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described .", "According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described . : . mild patients were identified with mild renal failure without an obvious oliguric stage; .", "mild patients were identified with mild renal failure without an obvious oliguric stage; . moderate patients were those with obvious symptoms of uremia, effusion bulbar conjunctiva , hemorrhage skin and mucous membrane , and renal failure with a typical oliguric stage; . severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and .", "severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and . critical patients exhibited ≥1 of the following signs during the illness: refractory shock, visceral hemorrhage, heart failure, pulmonary edema, brain edema, severe secondary infection, and severe renal failure with oliguria urine output, 50-500 ml/day for >5 days, anuria urine output, <50 ml/day for >2 days, or a BUN level of >42.84 mmol/l. Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group.", "Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group. The exclusion criteria for this study were patients with: . any other kidney disease, . diabetes mellitus, . autoimmune disease, . hematological disease, .", "diabetes mellitus, . autoimmune disease, . hematological disease, . cardiovascular disease, . viral hepatitis types A, B, C, D, or E , or . any other liver disease. In addition, no patients received corticosteroids or other immunomodulatory drugs during the study period . .", ". Genomic DNA was extracted from the peripheral blood of patients using the PureGene DNA Isolation kit Gentra Systems, Minneapolis, MN, USA . The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ .", "The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ . The PCR products were purified and sequenced using an Applied Biosystems 3730xl DNA Analyzer Thermo Scientific, Waltham, MA, USA . The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods .", "The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods . . Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA .", "Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA . The SuperScript III Platinum One-Step Quantitative RT-PCR System kit Invitrogen, Carlsbad, CA, USA was employed for the real-time RT-PCR assay. The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′.", "The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′. The synthetic S segment of the HTNV standard strain 76-118 RNA transcript was used as the quantitative calibrator. The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators.", "The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators. For each experiment, one aliquot of calibrated 76-118 standard was extracted in parallel with the clinical samples and serially 10-fold diluted with concentrations ranging from 10.56 to 2.56 log10 copies/ml. PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C.", "PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C. Fluorescence was read during the 72°C step of the final segment of every cycling program. HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator.", "HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator. A549 cells ATCC Cat# CRM-CCL-185, RRID:CVCL_0023 were grown in our laboratory in DMEM with 10% FBS Thermo Scientific, Waltham, MA, USA in a 5% CO2 incubator. Cells were used within passage 10 after primary culture.", "Cells were used within passage 10 after primary culture. HTNV strain 76-118 was cultured in Vero E6 cells ATCC Cat# CRL-1586, RRID:CVCL_0574 in our laboratory and titrated using an immunofluorescence staining assay for HTNV nucleocapsid protein NP as previously described . . The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method.", ". The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method. The recombinant human IFN-α2a was obtained from PBL Interferon Source Piscataway, NJ, USA and dissolved in the buffer provided by the manufacturer composition not disclosed . HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins.", "HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins. Subsequently, the virus solution was removed and fresh medium added to the cell culture. Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10.", "Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10. Puromycin 2 μg/ ml for HUVEC and 6 μg/ml for A549 cells was used to create cell lines stably expressing IFITMs. Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA .", "Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA . SiRNAs were purchased from Origene Rockville, MD, USA , and the sequences were not disclosed. Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA .", "Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA . Quantitative realtime PCR qPCR was performed using SYBR Premix Ex Taq II Takara Biotechnology Co., Dalian, China with a Bio-Rad iQ5 cycler Bio-Rad, Hercules, CA, USA . β-actin was used as the reference gene.", "β-actin was used as the reference gene. The primers Sangon Biotech, Shanghai, China were as follows: IFITM1 forward, 5′-ACTCCGTGAAGTCTAGGGACA-3′ and reverse, 5′-TGTCACAGAGCCGAATACCAG-3′ ; IFITM2 forward, 5′-ATCCCGGTAACCCGATCAC-3′ and reverse, 5′-CTTCCTGTCCCTAGACTTCAC-3′ ; IFITM3 forward, 5′-GGTCTTCGCTGGACACCAT-3′ and reverse, 5′-TGTCCCTAGACTTCACGGAGTA-3′ ; IFITM3 pre-mRNA forward, 5′-CATAGCACGCGGCTCT CAG-3′ and reverse, 5′-CGTCGCCAACCATCTTCCTG-3′ ; HTNV S segment forward, 5′-GCCTGGAGACCATCTGA AAG-3′ and reverse, 5′-AGTATCGGGACGACAAAGGA-3′ ; β-actin forward, 5′-GCTACGTCGCCCTGGACTTC-3′ and reverse, 5′-GTCATAGTCCGCCTAGAAGC-3′ ; NRIR forward, 5′-ATGGTTTTCTGGTGCCTTG-3′ and reverse, 5′-GGAGGTTAGAGGTGTCTGCTG-3′ ; NRAV forward, 5′-TCACTACTGCCCCAGGATCA-3′ and reverse, 5′-GGTGGTCACAGGACTCATGG-3′ . For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA .", "For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . MiR-130a level was determined using the gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control .", "U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control . . Because the pre-mRNA levels can represent the initial transcription rate . , the primers used to detect the pre-mRNA of IFITM3 were designed targeting the intron of IFITM3 as previously described . . IFITM3 has two exons and one intron.", ". IFITM3 has two exons and one intron. For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon.", "For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon. For qPCR of IFITM3 mRNA, the forward primers were positioned in the first exon, and the reverse primer was positioned at the beginning of the second exon . .", ". Because the basal expression of IFITM3 is low in A549 cells, we detected IFITM3 mRNA and pre-mRNA in A549 cells following IFN-α2a treatment 20 IU/ml for 12 h after the overexpression of NRIR. Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA .", "Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA . Equal amounts of protein 20 μg protein/lane were electrophoresed on a 10%-SDS-polyacrylamide gel and electrophoretically transferred to a polyvinylidene difluoride membrane Millipore, Billerica, MA, USA . After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C.", "After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C. The membranes were then washed and incubated with HRP-conjugated IgG antibody Cell Signaling Technology, Danvers, MA, USA for 1 h at room temperature. The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film.", "The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film. The blot densities were analyzed using the Quantity One software Bio-Rad, Hercules, CA, USA . In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS.", "In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS. Protease inhibitor cocktail Roche, Basel, Switzerland was added before use. The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 .", "The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 . At the indicated times post-HTNV infection, the cells were fixed with 4% PFA, incubated with 0.3% Triton X-100 Sigma-Aldrich, St. Louis, MO, USA , and blocked with 5% BSA for 1 h. Following incubation with a mouse monoclonal antibody against c-myc-tag Sigma-Aldrich, St. Louis, MO, USA, Sigma-Aldrich Cat# M5546 , IFITM3, lysosome-associated membrane glycoprotein 1 LAMP1, Cell Signaling Technology, Danvers, MA, USA , or HTNV NP at 37°C for 2 h, the cells were washed and incubated with anti-rabbit Ig conjugated to Alexa 555 and anti-mouse Ig conjugated to Alexa 488 Abcam, Cambridge, MA, USA secondary antibodies at room temperature for 1 h. The nuclei were counterstained with DAPI. An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images.", "An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images. hTnV binding and entry assay Cells transduced with IFITM3 or the empty vector were detached and washed extensively with cold PBS. The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation.", "The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation. Part of cells were washed extensively with ice-cold PBS and harvested for binding assay. Another part of cells were switched to 37°C for 2 h to allow HTNV entry.", "Another part of cells were switched to 37°C for 2 h to allow HTNV entry. The HTNV that remained on the cell surface was removed by treatment with proteinase K 0.1 mg/ml, Thermo Scientific, Waltham, MA, USA . To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl.", "To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl. Adsorption of HTNV moi = 1 was performed at 4°C for 1 h. The cells were then washed, and fusion of the virus with the plasma membrane was triggered by incubation in low pH medium 20 mM sodium succinate, pH = 5.5 for 10 min at 37°C. Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl .", "Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl . . qPCR analysis of the HTNV S segment was conducted to evaluate the influence of IFITMs on HTNV cell adhesion and HTNV entry. All data were expressed as the mean ± SEM.", "All data were expressed as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 GraphPad Software, La Jolla, CA, USA . For association analysis of the rs12252 allele and genotype, Fisher's exact test was used. Independent samples t-tests were used for normally distributed data.", "Independent samples t-tests were used for normally distributed data. Differences among groups were determined by one-way analysis of variance ANOVA with repeated measures, followed by Bonferroni's post hoc test. P < 0.05 was considered statistically significant.", "P < 0.05 was considered statistically significant. The iFiTM3 snP rs12252 c allele and cc genotype associated with severe hFrs Disease and a higher Plasma hTnV load To determine the clinical significance of IFITM3 SNP in HTNV infection, the relationship between rs12252 SNP and the severity of HFRS in 69 patients were examined. We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients.", "We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients. Then, we stratified these patients into mild and severe, based on the clinical assessment as described in Section \"Material and Methods. \" We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 .", "We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 . The frequency of rs12252 C in severe patients was also higher than those mild patients 68.29 vs. 46.43%, P = 0.013, Figures 1A,B; Table 2 . These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 .", "These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 . For genotypes, 43.90% of the severe patients carried the CC genotype, a significantly higher frequency than the control Han Chinese per 1,000 genomes sequence database 26.92% CC genotype, P = 0.03 as well as mildly infected patients 14.29%, P = 0.02, Figures 1A,B ; Table 2 . However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity.", "However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity. c The plasma HTNV load in CC genotype patients and CT/TT genotype patients, tested by qRCR analysis. Each symbol represents one individual patient.", "Each symbol represents one individual patient. Independent samples t-test was used to test the difference of HTNV load between groups. *P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population.", "*P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population. In addition, we also found that patients with CC genotype had higher plasma viral load in acute phase Figure 1C . These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS.", "These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS. Previous studies reveal that the truncated IFITM3 protein produced by SNP rs12252 C allele Figure 2A , the missing part stands for the truncated 21 amino acids from N-terminal of IFITM3, the intramembrane helix, and transmembrane helix was presented as boxes leads to an impaired anti-influenza activity . .", ". To test the functional significance of this polymorphism in HTNV infection, we transfected the majority T or minority C variant IFITM3 alleles that produce full-length or N-terminally truncated NΔ21 proteins Figure 2A with c-myc-tag to HUVEC and A549 cell using lentivirus vectors Figure 2B . Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material .", "Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material . Indeed, compared with the mock empty vector -infected control, the NΔ21 protein almost lost the ability to inhibit HTNV infection in both HUVEC and A549 cells Figures 2C,D ; Figure S3 in Supplementary Material . To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material .", "To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material . While we observed only a moderate upregulation of IFITM1, 2, and 3 mRNA and protein in HUVECs after more than 24 h postinfection; IFITM1, 2, and 3 mRNA, however, were only transiently upregulated in A549 cells and caused no significant change in protein level. We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually.", "We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually. The effect of siRNAs on the expression of target IFITMs was tested by qPCR in HUVECs Figure S2 in Supplementary Material , and the effect of the best oligo against each IFITMs IFITM1C, IFITM2A, IFITM3B was tested by Western blot in A549 Figure 4A and HUVEC cells Figure 4B . To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h .", "To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h . The cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were significantly suppressed in both HUVEC and A549 cells in response to IFN-α2a treatment. Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells.", "Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells. Knockdown of IFITM1 also partially restored the HTNV level in A549 cells Figures 4C,D . These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection.", "These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection. c-myc-tagged IFITM1, 2, and 3 were expressed in both HUVEC and A549 cells Figure 5A , and the cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were suppressed by IFITM3 overexpression in HUVEC cells Figures 5B-D . They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D .", "They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D . The inhibitory effect of IFITM3 was further confirmed by immunofluorescence analysis of HTNV NP Figure S3 in Supplementary Material . These results were in accordance with the above-described RNAi results.", "These results were in accordance with the above-described RNAi results. To determine whether IFITM3 inhibited HTNV binding or entry, HUVEC and A549 cells were incubated with HTNV moi = 1 at 4°C for 1 h, unbound virus was washed away, and HTNV RNA collected at this time point represents HTNV bound to the cell surface. After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus.", "After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus. As a positive control for inhibition of virus entry, we incubated a parallel group of cells with HTNV at pH = 5.5 as described in Section \"Materials and Methods.\" Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B .", "Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B . iFiTM3 Was Partially localized to laMP1 + late endosomes in the host cells To elucidate the mechanism of IFITM3 function, we investigated the subcellular localization of IFTIM3 in the host cells. IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C .", "IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C . The co-localization of IFITM3 and LAMP1 + late endosomes had also been found in A549 cells . . Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry .", "Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry . , this result provides an evidence for the anti-HTNV mechanism of IFITM3. LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies.", "LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies. We tested the change of previously reported regulators of IFITMs, such as NRAV, NRIR, and miR-130a after HTNV infection, among which NRIR was the only changed one downregulated after HTNV infection Figure 7A ; Figure S4 in Supplementary Material in HUVEC. However, the expression of NRIR was unchanged in A549 cells.", "However, the expression of NRIR was unchanged in A549 cells. We overexpressed NRIR in HUVEC and A549 cells using the pcDNA3.1 vector Figure 7B . Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E .", "Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E . These data suggest that lncRNA NRIR is a negative regulator of IFITM3 transcription. Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae.", "Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae. It causes HFRS, which is an important threat to public health worldwide. It is also a potential weapon for biological terrorism. Reservoir animals, usually rodents, are asymptomatic during persistent infection.", "Reservoir animals, usually rodents, are asymptomatic during persistent infection. Unlike in rodents, Hantavirus infection leads to HFRS and Hantavirus pulmonary syndrome HPS in humans . . The major clinical characteristics of HFRS include fever, hemorrhage, hypotension, and renal injury . , causing severe manifestations and death in some cases.", ", causing severe manifestations and death in some cases. The current standard of care for HFRS relies on symptomatic and supportive treatment. It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS ..", "It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS .. However, no direct antiviral medications are currently available for this illness. Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases.", "Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases. It has been reported that HTNV infection induces a late type I interferon response . . However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified.", "However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified. Therefore, identification of ISGs that are effective against HTNV is an attractive strategy to identify novel therapeutic targets. In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese.", "In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese. The rs12252 C allele and CC genotype are also found to be associated with higher plasma viral load in the early stage of HFRS. We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity.", "We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity. Interestingly, IFITM3 is found to restrict HTNV infection with a mechanism of cellular entry inhibition. Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry.", "Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry. In addition, we find that HTNV infection downregulated lncRNA NRIR 48 h post infection, which negatively regulates the transcription of IFITM3. Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS.", "Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. The antiviral properties of IFITM proteins were identified in 2009 in an RNAi screen for host factors that influence influenza virus replication . .", ". IFITM1, 2, and 3 have been demonstrated to possess antiviral activity in several studies. Everitt et al. demonstrated that the severity of influenza virus infection was greatly increased in IFITM3-knockout mice compared with wild-type animals . .", ". Different IFITM members have also been confirmed to inhibit the cellular entry of multiple virus families including filoviruses, rhabdoviruses, and flaviviruses 7, . . . 30 . For example, HIV-1 and HCV infection are inhibited by IFITM1 . . . . . It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry .", "It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry . . Recent studies suggested that the cellular location of different IFITMs may influence the range of viruses restricted by each protein . . IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles .", "IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles . , whereas IFITM3 confines influenza virus in acidified endosomal compartments . .", ", whereas IFITM3 confines influenza virus in acidified endosomal compartments . . Notably, retrovirus subvirus particles ISVPs , which do not require endosomal acidification for entry, are not inhibited by IFITM3 expression, suggesting that IFITM3 may function at the stage of endosomal entry . .", ". Studies utilizing cell-cell fusion assays have suggested that IFITM3 blocks the entry of enveloped virus by preventing the fusion of the viral membrane with a limiting membrane of the host cell, either the plasma membrane and/or the endosomal membranes. The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes .", "The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes . . In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells.", "In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells. Overexpression of IFITM3 inhibited HTNV infection to HUVEC and A549 cells. IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells.", "IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells. All these results suggest that IFITM3 is an important control factor under natural infection of HTNV. Our results also demonstrate that the effectiveness of IFITM3 is cell type-independent, which is in accordance with the results from similar viruses, such as RVFV . .", ". Binding and entry assays, conducted by controlling the temperature and pH, showed that IFITM3 did not significantly influence HTNV binding but inhibited HTNV entry into HUVEC and A549 cells. Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry.", "Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry. However, we failed in tracking the transportation of HTNV in infected cells possibly due to the lack of fluorescence-labeled virus. In addition, IFITM1 also suppressed HTNV infection in A549 cells.", "In addition, IFITM1 also suppressed HTNV infection in A549 cells. The mechanism underlying anti-HTNV effect of IFITM1 remains undetermined and deserves to be further explored. According to a recent study on the three-dimensional structure of IFITM3, there is a C-terminal transmembrane α-helix and a two-N-terminal intramembrane α-helices shown in Figure 2A as black boxes in IFITM3 . .", ". There are two splice variants that differ by the presence or absence of the first N-terminal 21 amino acids deleted part, shown in Figure 2A as red dotted line . Several SNPs including 13 non-synonymous, 13 synonymous, 1 in-frame stop, and 1 splice site acceptoraltering have been reported in the translated IFITM3 sequence . .", ". Among them, the rare SNP rs12252C allele of IFITM3 truncates the protein as described above, leading to a reduced inhibition of influenza virus infection in A549 cells . . We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro.", "We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro. In Northern European patients hospitalized with seasonal influenza or pandemic influenza A virus, increased homozygosity of the minor C allele of SNP rs12252 in IFITM3 was observed . .", ". In Chinese patients infected with influenza A H1N1 virus, there was also an increased frequency of the C allele and CC genotype of SNP rs12252 . . In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients.", "In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients. Patients carrying the CC genotype also had higher plasma viral loads compared with those with the CT/TT genotype. Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo.", "Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo. We speculate that the much higher level of CC allele at healthy population of Han Chinese compared with Caucasians may place the Chinese at a higher risk for developing severe illness upon HTNV infection, which needs further investigation. LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity.", "LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity. For example, lncRNA NEAT1 is reported to be upregulated by influenza virus or PolyI:C stimulation, which promotes IL-8 expression . .", ". lncRNA NRAV has been shown to negatively regulate the initial transcription of IFITM3 and Mx1 by affecting the histone modification of these genes . . lncRNA NRIR is a non-coding ISG, which has been reported to negatively regulate IFITM1 and Mx1 expression in HCV infection . . Mir-130a was also reported as a regulator of IFITM1 . .", ". Mir-130a was also reported as a regulator of IFITM1 . . In this analysis, lncRNA NRIR was downregulated in HUVECs after HTNV infection for 48 h, overexpression of NRIR negatively regulates the initial transcription of IFITM3, evidenced by the decreased pre-mRNA as well as mRNA levels. NRIR overexpression also facilitated HTNV infection.", "NRIR overexpression also facilitated HTNV infection. These results indicate that the downregulation of NRIR after HTNV infection is possibly involved in the activation of innate immune responses against HTNV infection. We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study.", "We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study. Another lncRNA that can regulate IFITM3, i.e., NRAV NR_038854 , remained unchanged after HTNV infection Figures S4A,B in Supplementary Material . Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material .", "Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material . In conclusion, this study revealed a critical role for IFITM3 in HTNV infection. We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells.", "We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells. In addition, we discovered that the IFITM3 SNP rs12252 C allele and CC genotype correlates with the plasma HTNV load and the severity of HFRS; and the rs12252 C allele produces a truncated IFITM3 protein NΔ21 that attenuates its anti-HTNV function. These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease.", "These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease. aUThOr cOnTribUTiOns ZX-y, BP-y, YC-t, and MH-w performed the experiments; WP-z, BX-f, LY-f, ZY, and JZ-s designed the research; HC-x, YW, and WX analyzed the data; TK and ZC-m provided clinical data; ZX-y and BP-y wrote the paper." ]

[ "Hantaan virus HTNV causes hemorrhagic fever with renal syndrome HFRS . Previous studies have identified interferon-induced transmembrane proteins IFITMs as an interferon-stimulated gene family. However, the role of IFITMs in HTNV infection is unclear.", "However, the role of IFITMs in HTNV infection is unclear. In this study, we observed that IFITM3 single nucleotide polymorphisms SNP rs12252 C allele and CC genotype associated with the disease severity and HTNV load in the plasma of HFRS patients. In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity.", "In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity. We also proved that IFITM3 was able to inhibit HTNV infection in both HUVEC and A549 cells by overexpression and RNAi assays, likely via a mechanism of inhibiting virus entry demonstrated by binding and entry assay. Localization of IFITM3 in late endosomes was also observed.", "Localization of IFITM3 in late endosomes was also observed. In addition, we demonstrated that the transcription of IFITM3 is negatively regulated by an lncRNA negative regulator of interferon response NRIR . Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS.", "Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS. Text: associates with the severity of disease, indicating the importance of viremia in the pathogenesis of HFRS . .", ". Therefore, further studies of host factors limiting HTNV infection and influencing antiviral response as well as disease progression are clinically significant and timely. The human family of interferon-induced transmembrane proteins IFITMs was discovered 25 years ago to consist of interferon-stimulated genes ISGs . .", ". This family includes five members, namely, IFITM1, 2, 3, 5, and 10, among which IFITM1, 2, and 3 possess antiviral activity . . Different IFITM proteins have different antiviral spectrum . . For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice .", "For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice . , and it also inhibits multiple viruses, including filoviruses, rhabdoviruses, flaviviruses, and even Ebola and Zika virus . . . . . . The antiviral mechanism of IFITM3 is thought to be the restriction of viral entry into cells . .", ". Single nucleotide polymorphisms SNPs are single nucleotide variations in a genetic sequence that occur at an appreciable frequency in the population. Several SNPs has been identified in IFITM3, among which the rs12252 site with C allele results in a N-terminal truncation of IFITM3 protein, leading to impaired inhibition of influenza virus in vitro . .", ". Notably, the frequencies of rs12252 C allele and CC genotype correlate with disease severity in patients infected with influenza virus . . HTNV has been shown to induce a type I interferon response though in later time postinfection . . While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection .", "While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection . , however, the effect of IFITMs on HTNV infection in human cell lines and its role in HFRS still remain unknown. LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators.", "LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators. Some lncRNAs have been shown to play a role in innate immunity . . Among them, negative regulator of interferon response NRIR lncRNA NRIR, also known as lncRNA-CMPK2 is a non-coding ISG that negatively regulates IFITM1 and Mx1 expression in HCV infection . .", ". Notably, IFITM3 is largely homologous to IFITM1, but the role of NRIR in the regulation of IFITM3 in HTNV infection remains unclear. In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans.", "In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans. We provide primary evidence suggesting that IFITM3, regulated by NRIR, can inhibit HTNV infection and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection.", "This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection. This study was conducted in accordance with the recommendations of the biomedical research guidelines involving human participants established by the National Health and Family Planning Commission of China. The Institutional Ethics Committee of Tangdu Hospital approved this study.", "The Institutional Ethics Committee of Tangdu Hospital approved this study. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained.", "Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained. Sixty-nine HFRS patients admitted into the Department of Infectious Diseases, Tangdu Hospital between October 2014 and March 2016 were enrolled in this study. All patients were Han Chinese.", "All patients were Han Chinese. The diagnosis of HFRS was made based on typical symptoms and signs as well as positive IgM and IgG antibodies against HTNV in the serum assessed by enzyme linked immunosorbent assay ELISA in our department. The classification of HFRS severity and the exclusion criteria were described as follows .", "The classification of HFRS severity and the exclusion criteria were described as follows . : white blood cells WBC , platelets PLT , blood urea nitrogen BUN , serum creatinine Scr , and heteromorphic lymphocytes that were tested by the Department of Clinical Laboratory shown in Table 1 . According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described .", "According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described . : . mild patients were identified with mild renal failure without an obvious oliguric stage; .", "mild patients were identified with mild renal failure without an obvious oliguric stage; . moderate patients were those with obvious symptoms of uremia, effusion bulbar conjunctiva , hemorrhage skin and mucous membrane , and renal failure with a typical oliguric stage; . severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and .", "severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and . critical patients exhibited ≥1 of the following signs during the illness: refractory shock, visceral hemorrhage, heart failure, pulmonary edema, brain edema, severe secondary infection, and severe renal failure with oliguria urine output, 50-500 ml/day for >5 days, anuria urine output, <50 ml/day for >2 days, or a BUN level of >42.84 mmol/l. Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group.", "Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group. The exclusion criteria for this study were patients with: . any other kidney disease, . diabetes mellitus, . autoimmune disease, . hematological disease, .", "diabetes mellitus, . autoimmune disease, . hematological disease, . cardiovascular disease, . viral hepatitis types A, B, C, D, or E , or . any other liver disease. In addition, no patients received corticosteroids or other immunomodulatory drugs during the study period . .", ". Genomic DNA was extracted from the peripheral blood of patients using the PureGene DNA Isolation kit Gentra Systems, Minneapolis, MN, USA . The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ .", "The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ . The PCR products were purified and sequenced using an Applied Biosystems 3730xl DNA Analyzer Thermo Scientific, Waltham, MA, USA . The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods .", "The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods . . Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA .", "Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA . The SuperScript III Platinum One-Step Quantitative RT-PCR System kit Invitrogen, Carlsbad, CA, USA was employed for the real-time RT-PCR assay. The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′.", "The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′. The synthetic S segment of the HTNV standard strain 76-118 RNA transcript was used as the quantitative calibrator. The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators.", "The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators. For each experiment, one aliquot of calibrated 76-118 standard was extracted in parallel with the clinical samples and serially 10-fold diluted with concentrations ranging from 10.56 to 2.56 log10 copies/ml. PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C.", "PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C. Fluorescence was read during the 72°C step of the final segment of every cycling program. HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator.", "HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator. A549 cells ATCC Cat# CRM-CCL-185, RRID:CVCL_0023 were grown in our laboratory in DMEM with 10% FBS Thermo Scientific, Waltham, MA, USA in a 5% CO2 incubator. Cells were used within passage 10 after primary culture.", "Cells were used within passage 10 after primary culture. HTNV strain 76-118 was cultured in Vero E6 cells ATCC Cat# CRL-1586, RRID:CVCL_0574 in our laboratory and titrated using an immunofluorescence staining assay for HTNV nucleocapsid protein NP as previously described . . The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method.", ". The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method. The recombinant human IFN-α2a was obtained from PBL Interferon Source Piscataway, NJ, USA and dissolved in the buffer provided by the manufacturer composition not disclosed . HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins.", "HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins. Subsequently, the virus solution was removed and fresh medium added to the cell culture. Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10.", "Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10. Puromycin 2 μg/ ml for HUVEC and 6 μg/ml for A549 cells was used to create cell lines stably expressing IFITMs. Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA .", "Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA . SiRNAs were purchased from Origene Rockville, MD, USA , and the sequences were not disclosed. Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA .", "Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA . Quantitative realtime PCR qPCR was performed using SYBR Premix Ex Taq II Takara Biotechnology Co., Dalian, China with a Bio-Rad iQ5 cycler Bio-Rad, Hercules, CA, USA . β-actin was used as the reference gene.", "β-actin was used as the reference gene. The primers Sangon Biotech, Shanghai, China were as follows: IFITM1 forward, 5′-ACTCCGTGAAGTCTAGGGACA-3′ and reverse, 5′-TGTCACAGAGCCGAATACCAG-3′ ; IFITM2 forward, 5′-ATCCCGGTAACCCGATCAC-3′ and reverse, 5′-CTTCCTGTCCCTAGACTTCAC-3′ ; IFITM3 forward, 5′-GGTCTTCGCTGGACACCAT-3′ and reverse, 5′-TGTCCCTAGACTTCACGGAGTA-3′ ; IFITM3 pre-mRNA forward, 5′-CATAGCACGCGGCTCT CAG-3′ and reverse, 5′-CGTCGCCAACCATCTTCCTG-3′ ; HTNV S segment forward, 5′-GCCTGGAGACCATCTGA AAG-3′ and reverse, 5′-AGTATCGGGACGACAAAGGA-3′ ; β-actin forward, 5′-GCTACGTCGCCCTGGACTTC-3′ and reverse, 5′-GTCATAGTCCGCCTAGAAGC-3′ ; NRIR forward, 5′-ATGGTTTTCTGGTGCCTTG-3′ and reverse, 5′-GGAGGTTAGAGGTGTCTGCTG-3′ ; NRAV forward, 5′-TCACTACTGCCCCAGGATCA-3′ and reverse, 5′-GGTGGTCACAGGACTCATGG-3′ . For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA .", "For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . MiR-130a level was determined using the gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control .", "U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control . . Because the pre-mRNA levels can represent the initial transcription rate . , the primers used to detect the pre-mRNA of IFITM3 were designed targeting the intron of IFITM3 as previously described . . IFITM3 has two exons and one intron.", ". IFITM3 has two exons and one intron. For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon.", "For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon. For qPCR of IFITM3 mRNA, the forward primers were positioned in the first exon, and the reverse primer was positioned at the beginning of the second exon . .", ". Because the basal expression of IFITM3 is low in A549 cells, we detected IFITM3 mRNA and pre-mRNA in A549 cells following IFN-α2a treatment 20 IU/ml for 12 h after the overexpression of NRIR. Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA .", "Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA . Equal amounts of protein 20 μg protein/lane were electrophoresed on a 10%-SDS-polyacrylamide gel and electrophoretically transferred to a polyvinylidene difluoride membrane Millipore, Billerica, MA, USA . After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C.", "After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C. The membranes were then washed and incubated with HRP-conjugated IgG antibody Cell Signaling Technology, Danvers, MA, USA for 1 h at room temperature. The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film.", "The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film. The blot densities were analyzed using the Quantity One software Bio-Rad, Hercules, CA, USA . In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS.", "In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS. Protease inhibitor cocktail Roche, Basel, Switzerland was added before use. The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 .", "The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 . At the indicated times post-HTNV infection, the cells were fixed with 4% PFA, incubated with 0.3% Triton X-100 Sigma-Aldrich, St. Louis, MO, USA , and blocked with 5% BSA for 1 h. Following incubation with a mouse monoclonal antibody against c-myc-tag Sigma-Aldrich, St. Louis, MO, USA, Sigma-Aldrich Cat# M5546 , IFITM3, lysosome-associated membrane glycoprotein 1 LAMP1, Cell Signaling Technology, Danvers, MA, USA , or HTNV NP at 37°C for 2 h, the cells were washed and incubated with anti-rabbit Ig conjugated to Alexa 555 and anti-mouse Ig conjugated to Alexa 488 Abcam, Cambridge, MA, USA secondary antibodies at room temperature for 1 h. The nuclei were counterstained with DAPI. An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images.", "An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images. hTnV binding and entry assay Cells transduced with IFITM3 or the empty vector were detached and washed extensively with cold PBS. The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation.", "The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation. Part of cells were washed extensively with ice-cold PBS and harvested for binding assay. Another part of cells were switched to 37°C for 2 h to allow HTNV entry.", "Another part of cells were switched to 37°C for 2 h to allow HTNV entry. The HTNV that remained on the cell surface was removed by treatment with proteinase K 0.1 mg/ml, Thermo Scientific, Waltham, MA, USA . To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl.", "To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl. Adsorption of HTNV moi = 1 was performed at 4°C for 1 h. The cells were then washed, and fusion of the virus with the plasma membrane was triggered by incubation in low pH medium 20 mM sodium succinate, pH = 5.5 for 10 min at 37°C. Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl .", "Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl . . qPCR analysis of the HTNV S segment was conducted to evaluate the influence of IFITMs on HTNV cell adhesion and HTNV entry. All data were expressed as the mean ± SEM.", "All data were expressed as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 GraphPad Software, La Jolla, CA, USA . For association analysis of the rs12252 allele and genotype, Fisher's exact test was used. Independent samples t-tests were used for normally distributed data.", "Independent samples t-tests were used for normally distributed data. Differences among groups were determined by one-way analysis of variance ANOVA with repeated measures, followed by Bonferroni's post hoc test. P < 0.05 was considered statistically significant.", "P < 0.05 was considered statistically significant. The iFiTM3 snP rs12252 c allele and cc genotype associated with severe hFrs Disease and a higher Plasma hTnV load To determine the clinical significance of IFITM3 SNP in HTNV infection, the relationship between rs12252 SNP and the severity of HFRS in 69 patients were examined. We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients.", "We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients. Then, we stratified these patients into mild and severe, based on the clinical assessment as described in Section \"Material and Methods. \" We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 .", "We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 . The frequency of rs12252 C in severe patients was also higher than those mild patients 68.29 vs. 46.43%, P = 0.013, Figures 1A,B; Table 2 . These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 .", "These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 . For genotypes, 43.90% of the severe patients carried the CC genotype, a significantly higher frequency than the control Han Chinese per 1,000 genomes sequence database 26.92% CC genotype, P = 0.03 as well as mildly infected patients 14.29%, P = 0.02, Figures 1A,B ; Table 2 . However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity.", "However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity. c The plasma HTNV load in CC genotype patients and CT/TT genotype patients, tested by qRCR analysis. Each symbol represents one individual patient.", "Each symbol represents one individual patient. Independent samples t-test was used to test the difference of HTNV load between groups. *P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population.", "*P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population. In addition, we also found that patients with CC genotype had higher plasma viral load in acute phase Figure 1C . These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS.", "These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS. Previous studies reveal that the truncated IFITM3 protein produced by SNP rs12252 C allele Figure 2A , the missing part stands for the truncated 21 amino acids from N-terminal of IFITM3, the intramembrane helix, and transmembrane helix was presented as boxes leads to an impaired anti-influenza activity . .", ". To test the functional significance of this polymorphism in HTNV infection, we transfected the majority T or minority C variant IFITM3 alleles that produce full-length or N-terminally truncated NΔ21 proteins Figure 2A with c-myc-tag to HUVEC and A549 cell using lentivirus vectors Figure 2B . Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material .", "Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material . Indeed, compared with the mock empty vector -infected control, the NΔ21 protein almost lost the ability to inhibit HTNV infection in both HUVEC and A549 cells Figures 2C,D ; Figure S3 in Supplementary Material . To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material .", "To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material . While we observed only a moderate upregulation of IFITM1, 2, and 3 mRNA and protein in HUVECs after more than 24 h postinfection; IFITM1, 2, and 3 mRNA, however, were only transiently upregulated in A549 cells and caused no significant change in protein level. We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually.", "We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually. The effect of siRNAs on the expression of target IFITMs was tested by qPCR in HUVECs Figure S2 in Supplementary Material , and the effect of the best oligo against each IFITMs IFITM1C, IFITM2A, IFITM3B was tested by Western blot in A549 Figure 4A and HUVEC cells Figure 4B . To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h .", "To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h . The cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were significantly suppressed in both HUVEC and A549 cells in response to IFN-α2a treatment. Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells.", "Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells. Knockdown of IFITM1 also partially restored the HTNV level in A549 cells Figures 4C,D . These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection.", "These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection. c-myc-tagged IFITM1, 2, and 3 were expressed in both HUVEC and A549 cells Figure 5A , and the cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were suppressed by IFITM3 overexpression in HUVEC cells Figures 5B-D . They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D .", "They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D . The inhibitory effect of IFITM3 was further confirmed by immunofluorescence analysis of HTNV NP Figure S3 in Supplementary Material . These results were in accordance with the above-described RNAi results.", "These results were in accordance with the above-described RNAi results. To determine whether IFITM3 inhibited HTNV binding or entry, HUVEC and A549 cells were incubated with HTNV moi = 1 at 4°C for 1 h, unbound virus was washed away, and HTNV RNA collected at this time point represents HTNV bound to the cell surface. After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus.", "After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus. As a positive control for inhibition of virus entry, we incubated a parallel group of cells with HTNV at pH = 5.5 as described in Section \"Materials and Methods.\" Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B .", "Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B . iFiTM3 Was Partially localized to laMP1 + late endosomes in the host cells To elucidate the mechanism of IFITM3 function, we investigated the subcellular localization of IFTIM3 in the host cells. IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C .", "IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C . The co-localization of IFITM3 and LAMP1 + late endosomes had also been found in A549 cells . . Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry .", "Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry . , this result provides an evidence for the anti-HTNV mechanism of IFITM3. LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies.", "LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies. We tested the change of previously reported regulators of IFITMs, such as NRAV, NRIR, and miR-130a after HTNV infection, among which NRIR was the only changed one downregulated after HTNV infection Figure 7A ; Figure S4 in Supplementary Material in HUVEC. However, the expression of NRIR was unchanged in A549 cells.", "However, the expression of NRIR was unchanged in A549 cells. We overexpressed NRIR in HUVEC and A549 cells using the pcDNA3.1 vector Figure 7B . Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E .", "Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E . These data suggest that lncRNA NRIR is a negative regulator of IFITM3 transcription. Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae.", "Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae. It causes HFRS, which is an important threat to public health worldwide. It is also a potential weapon for biological terrorism. Reservoir animals, usually rodents, are asymptomatic during persistent infection.", "Reservoir animals, usually rodents, are asymptomatic during persistent infection. Unlike in rodents, Hantavirus infection leads to HFRS and Hantavirus pulmonary syndrome HPS in humans . . The major clinical characteristics of HFRS include fever, hemorrhage, hypotension, and renal injury . , causing severe manifestations and death in some cases.", ", causing severe manifestations and death in some cases. The current standard of care for HFRS relies on symptomatic and supportive treatment. It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS ..", "It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS .. However, no direct antiviral medications are currently available for this illness. Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases.", "Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases. It has been reported that HTNV infection induces a late type I interferon response . . However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified.", "However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified. Therefore, identification of ISGs that are effective against HTNV is an attractive strategy to identify novel therapeutic targets. In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese.", "In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese. The rs12252 C allele and CC genotype are also found to be associated with higher plasma viral load in the early stage of HFRS. We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity.", "We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity. Interestingly, IFITM3 is found to restrict HTNV infection with a mechanism of cellular entry inhibition. Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry.", "Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry. In addition, we find that HTNV infection downregulated lncRNA NRIR 48 h post infection, which negatively regulates the transcription of IFITM3. Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS.", "Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. The antiviral properties of IFITM proteins were identified in 2009 in an RNAi screen for host factors that influence influenza virus replication . .", ". IFITM1, 2, and 3 have been demonstrated to possess antiviral activity in several studies. Everitt et al. demonstrated that the severity of influenza virus infection was greatly increased in IFITM3-knockout mice compared with wild-type animals . .", ". Different IFITM members have also been confirmed to inhibit the cellular entry of multiple virus families including filoviruses, rhabdoviruses, and flaviviruses 7, . . . 30 . For example, HIV-1 and HCV infection are inhibited by IFITM1 . . . . . It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry .", "It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry . . Recent studies suggested that the cellular location of different IFITMs may influence the range of viruses restricted by each protein . . IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles .", "IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles . , whereas IFITM3 confines influenza virus in acidified endosomal compartments . .", ", whereas IFITM3 confines influenza virus in acidified endosomal compartments . . Notably, retrovirus subvirus particles ISVPs , which do not require endosomal acidification for entry, are not inhibited by IFITM3 expression, suggesting that IFITM3 may function at the stage of endosomal entry . .", ". Studies utilizing cell-cell fusion assays have suggested that IFITM3 blocks the entry of enveloped virus by preventing the fusion of the viral membrane with a limiting membrane of the host cell, either the plasma membrane and/or the endosomal membranes. The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes .", "The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes . . In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells.", "In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells. Overexpression of IFITM3 inhibited HTNV infection to HUVEC and A549 cells. IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells.", "IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells. All these results suggest that IFITM3 is an important control factor under natural infection of HTNV. Our results also demonstrate that the effectiveness of IFITM3 is cell type-independent, which is in accordance with the results from similar viruses, such as RVFV . .", ". Binding and entry assays, conducted by controlling the temperature and pH, showed that IFITM3 did not significantly influence HTNV binding but inhibited HTNV entry into HUVEC and A549 cells. Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry.", "Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry. However, we failed in tracking the transportation of HTNV in infected cells possibly due to the lack of fluorescence-labeled virus. In addition, IFITM1 also suppressed HTNV infection in A549 cells.", "In addition, IFITM1 also suppressed HTNV infection in A549 cells. The mechanism underlying anti-HTNV effect of IFITM1 remains undetermined and deserves to be further explored. According to a recent study on the three-dimensional structure of IFITM3, there is a C-terminal transmembrane α-helix and a two-N-terminal intramembrane α-helices shown in Figure 2A as black boxes in IFITM3 . .", ". There are two splice variants that differ by the presence or absence of the first N-terminal 21 amino acids deleted part, shown in Figure 2A as red dotted line . Several SNPs including 13 non-synonymous, 13 synonymous, 1 in-frame stop, and 1 splice site acceptoraltering have been reported in the translated IFITM3 sequence . .", ". Among them, the rare SNP rs12252C allele of IFITM3 truncates the protein as described above, leading to a reduced inhibition of influenza virus infection in A549 cells . . We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro.", "We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro. In Northern European patients hospitalized with seasonal influenza or pandemic influenza A virus, increased homozygosity of the minor C allele of SNP rs12252 in IFITM3 was observed . .", ". In Chinese patients infected with influenza A H1N1 virus, there was also an increased frequency of the C allele and CC genotype of SNP rs12252 . . In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients.", "In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients. Patients carrying the CC genotype also had higher plasma viral loads compared with those with the CT/TT genotype. Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo.", "Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo. We speculate that the much higher level of CC allele at healthy population of Han Chinese compared with Caucasians may place the Chinese at a higher risk for developing severe illness upon HTNV infection, which needs further investigation. LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity.", "LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity. For example, lncRNA NEAT1 is reported to be upregulated by influenza virus or PolyI:C stimulation, which promotes IL-8 expression . .", ". lncRNA NRAV has been shown to negatively regulate the initial transcription of IFITM3 and Mx1 by affecting the histone modification of these genes . . lncRNA NRIR is a non-coding ISG, which has been reported to negatively regulate IFITM1 and Mx1 expression in HCV infection . . Mir-130a was also reported as a regulator of IFITM1 . .", ". Mir-130a was also reported as a regulator of IFITM1 . . In this analysis, lncRNA NRIR was downregulated in HUVECs after HTNV infection for 48 h, overexpression of NRIR negatively regulates the initial transcription of IFITM3, evidenced by the decreased pre-mRNA as well as mRNA levels. NRIR overexpression also facilitated HTNV infection.", "NRIR overexpression also facilitated HTNV infection. These results indicate that the downregulation of NRIR after HTNV infection is possibly involved in the activation of innate immune responses against HTNV infection. We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study.", "We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study. Another lncRNA that can regulate IFITM3, i.e., NRAV NR_038854 , remained unchanged after HTNV infection Figures S4A,B in Supplementary Material . Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material .", "Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material . In conclusion, this study revealed a critical role for IFITM3 in HTNV infection. We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells.", "We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells. In addition, we discovered that the IFITM3 SNP rs12252 C allele and CC genotype correlates with the plasma HTNV load and the severity of HFRS; and the rs12252 C allele produces a truncated IFITM3 protein NΔ21 that attenuates its anti-HTNV function. These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease.", "These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease. aUThOr cOnTribUTiOns ZX-y, BP-y, YC-t, and MH-w performed the experiments; WP-z, BX-f, LY-f, ZY, and JZ-s designed the research; HC-x, YW, and WX analyzed the data; TK and ZC-m provided clinical data; ZX-y and BP-y wrote the paper." ]

[ "Hantaan virus HTNV causes hemorrhagic fever with renal syndrome HFRS . Previous studies have identified interferon-induced transmembrane proteins IFITMs as an interferon-stimulated gene family. However, the role of IFITMs in HTNV infection is unclear.", "However, the role of IFITMs in HTNV infection is unclear. In this study, we observed that IFITM3 single nucleotide polymorphisms SNP rs12252 C allele and CC genotype associated with the disease severity and HTNV load in the plasma of HFRS patients. In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity.", "In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity. We also proved that IFITM3 was able to inhibit HTNV infection in both HUVEC and A549 cells by overexpression and RNAi assays, likely via a mechanism of inhibiting virus entry demonstrated by binding and entry assay. Localization of IFITM3 in late endosomes was also observed.", "Localization of IFITM3 in late endosomes was also observed. In addition, we demonstrated that the transcription of IFITM3 is negatively regulated by an lncRNA negative regulator of interferon response NRIR . Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS.", "Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS. Text: associates with the severity of disease, indicating the importance of viremia in the pathogenesis of HFRS . .", ". Therefore, further studies of host factors limiting HTNV infection and influencing antiviral response as well as disease progression are clinically significant and timely. The human family of interferon-induced transmembrane proteins IFITMs was discovered 25 years ago to consist of interferon-stimulated genes ISGs . .", ". This family includes five members, namely, IFITM1, 2, 3, 5, and 10, among which IFITM1, 2, and 3 possess antiviral activity . . Different IFITM proteins have different antiviral spectrum . . For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice .", "For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice . , and it also inhibits multiple viruses, including filoviruses, rhabdoviruses, flaviviruses, and even Ebola and Zika virus . . . . . . The antiviral mechanism of IFITM3 is thought to be the restriction of viral entry into cells . .", ". Single nucleotide polymorphisms SNPs are single nucleotide variations in a genetic sequence that occur at an appreciable frequency in the population. Several SNPs has been identified in IFITM3, among which the rs12252 site with C allele results in a N-terminal truncation of IFITM3 protein, leading to impaired inhibition of influenza virus in vitro . .", ". Notably, the frequencies of rs12252 C allele and CC genotype correlate with disease severity in patients infected with influenza virus . . HTNV has been shown to induce a type I interferon response though in later time postinfection . . While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection .", "While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection . , however, the effect of IFITMs on HTNV infection in human cell lines and its role in HFRS still remain unknown. LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators.", "LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators. Some lncRNAs have been shown to play a role in innate immunity . . Among them, negative regulator of interferon response NRIR lncRNA NRIR, also known as lncRNA-CMPK2 is a non-coding ISG that negatively regulates IFITM1 and Mx1 expression in HCV infection . .", ". Notably, IFITM3 is largely homologous to IFITM1, but the role of NRIR in the regulation of IFITM3 in HTNV infection remains unclear. In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans.", "In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans. We provide primary evidence suggesting that IFITM3, regulated by NRIR, can inhibit HTNV infection and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection.", "This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection. This study was conducted in accordance with the recommendations of the biomedical research guidelines involving human participants established by the National Health and Family Planning Commission of China. The Institutional Ethics Committee of Tangdu Hospital approved this study.", "The Institutional Ethics Committee of Tangdu Hospital approved this study. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained.", "Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained. Sixty-nine HFRS patients admitted into the Department of Infectious Diseases, Tangdu Hospital between October 2014 and March 2016 were enrolled in this study. All patients were Han Chinese.", "All patients were Han Chinese. The diagnosis of HFRS was made based on typical symptoms and signs as well as positive IgM and IgG antibodies against HTNV in the serum assessed by enzyme linked immunosorbent assay ELISA in our department. The classification of HFRS severity and the exclusion criteria were described as follows .", "The classification of HFRS severity and the exclusion criteria were described as follows . : white blood cells WBC , platelets PLT , blood urea nitrogen BUN , serum creatinine Scr , and heteromorphic lymphocytes that were tested by the Department of Clinical Laboratory shown in Table 1 . According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described .", "According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described . : . mild patients were identified with mild renal failure without an obvious oliguric stage; .", "mild patients were identified with mild renal failure without an obvious oliguric stage; . moderate patients were those with obvious symptoms of uremia, effusion bulbar conjunctiva , hemorrhage skin and mucous membrane , and renal failure with a typical oliguric stage; . severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and .", "severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and . critical patients exhibited ≥1 of the following signs during the illness: refractory shock, visceral hemorrhage, heart failure, pulmonary edema, brain edema, severe secondary infection, and severe renal failure with oliguria urine output, 50-500 ml/day for >5 days, anuria urine output, <50 ml/day for >2 days, or a BUN level of >42.84 mmol/l. Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group.", "Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group. The exclusion criteria for this study were patients with: . any other kidney disease, . diabetes mellitus, . autoimmune disease, . hematological disease, .", "diabetes mellitus, . autoimmune disease, . hematological disease, . cardiovascular disease, . viral hepatitis types A, B, C, D, or E , or . any other liver disease. In addition, no patients received corticosteroids or other immunomodulatory drugs during the study period . .", ". Genomic DNA was extracted from the peripheral blood of patients using the PureGene DNA Isolation kit Gentra Systems, Minneapolis, MN, USA . The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ .", "The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ . The PCR products were purified and sequenced using an Applied Biosystems 3730xl DNA Analyzer Thermo Scientific, Waltham, MA, USA . The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods .", "The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods . . Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA .", "Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA . The SuperScript III Platinum One-Step Quantitative RT-PCR System kit Invitrogen, Carlsbad, CA, USA was employed for the real-time RT-PCR assay. The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′.", "The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′. The synthetic S segment of the HTNV standard strain 76-118 RNA transcript was used as the quantitative calibrator. The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators.", "The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators. For each experiment, one aliquot of calibrated 76-118 standard was extracted in parallel with the clinical samples and serially 10-fold diluted with concentrations ranging from 10.56 to 2.56 log10 copies/ml. PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C.", "PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C. Fluorescence was read during the 72°C step of the final segment of every cycling program. HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator.", "HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator. A549 cells ATCC Cat# CRM-CCL-185, RRID:CVCL_0023 were grown in our laboratory in DMEM with 10% FBS Thermo Scientific, Waltham, MA, USA in a 5% CO2 incubator. Cells were used within passage 10 after primary culture.", "Cells were used within passage 10 after primary culture. HTNV strain 76-118 was cultured in Vero E6 cells ATCC Cat# CRL-1586, RRID:CVCL_0574 in our laboratory and titrated using an immunofluorescence staining assay for HTNV nucleocapsid protein NP as previously described . . The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method.", ". The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method. The recombinant human IFN-α2a was obtained from PBL Interferon Source Piscataway, NJ, USA and dissolved in the buffer provided by the manufacturer composition not disclosed . HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins.", "HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins. Subsequently, the virus solution was removed and fresh medium added to the cell culture. Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10.", "Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10. Puromycin 2 μg/ ml for HUVEC and 6 μg/ml for A549 cells was used to create cell lines stably expressing IFITMs. Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA .", "Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA . SiRNAs were purchased from Origene Rockville, MD, USA , and the sequences were not disclosed. Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA .", "Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA . Quantitative realtime PCR qPCR was performed using SYBR Premix Ex Taq II Takara Biotechnology Co., Dalian, China with a Bio-Rad iQ5 cycler Bio-Rad, Hercules, CA, USA . β-actin was used as the reference gene.", "β-actin was used as the reference gene. The primers Sangon Biotech, Shanghai, China were as follows: IFITM1 forward, 5′-ACTCCGTGAAGTCTAGGGACA-3′ and reverse, 5′-TGTCACAGAGCCGAATACCAG-3′ ; IFITM2 forward, 5′-ATCCCGGTAACCCGATCAC-3′ and reverse, 5′-CTTCCTGTCCCTAGACTTCAC-3′ ; IFITM3 forward, 5′-GGTCTTCGCTGGACACCAT-3′ and reverse, 5′-TGTCCCTAGACTTCACGGAGTA-3′ ; IFITM3 pre-mRNA forward, 5′-CATAGCACGCGGCTCT CAG-3′ and reverse, 5′-CGTCGCCAACCATCTTCCTG-3′ ; HTNV S segment forward, 5′-GCCTGGAGACCATCTGA AAG-3′ and reverse, 5′-AGTATCGGGACGACAAAGGA-3′ ; β-actin forward, 5′-GCTACGTCGCCCTGGACTTC-3′ and reverse, 5′-GTCATAGTCCGCCTAGAAGC-3′ ; NRIR forward, 5′-ATGGTTTTCTGGTGCCTTG-3′ and reverse, 5′-GGAGGTTAGAGGTGTCTGCTG-3′ ; NRAV forward, 5′-TCACTACTGCCCCAGGATCA-3′ and reverse, 5′-GGTGGTCACAGGACTCATGG-3′ . For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA .", "For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . MiR-130a level was determined using the gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control .", "U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control . . Because the pre-mRNA levels can represent the initial transcription rate . , the primers used to detect the pre-mRNA of IFITM3 were designed targeting the intron of IFITM3 as previously described . . IFITM3 has two exons and one intron.", ". IFITM3 has two exons and one intron. For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon.", "For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon. For qPCR of IFITM3 mRNA, the forward primers were positioned in the first exon, and the reverse primer was positioned at the beginning of the second exon . .", ". Because the basal expression of IFITM3 is low in A549 cells, we detected IFITM3 mRNA and pre-mRNA in A549 cells following IFN-α2a treatment 20 IU/ml for 12 h after the overexpression of NRIR. Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA .", "Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA . Equal amounts of protein 20 μg protein/lane were electrophoresed on a 10%-SDS-polyacrylamide gel and electrophoretically transferred to a polyvinylidene difluoride membrane Millipore, Billerica, MA, USA . After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C.", "After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C. The membranes were then washed and incubated with HRP-conjugated IgG antibody Cell Signaling Technology, Danvers, MA, USA for 1 h at room temperature. The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film.", "The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film. The blot densities were analyzed using the Quantity One software Bio-Rad, Hercules, CA, USA . In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS.", "In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS. Protease inhibitor cocktail Roche, Basel, Switzerland was added before use. The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 .", "The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 . At the indicated times post-HTNV infection, the cells were fixed with 4% PFA, incubated with 0.3% Triton X-100 Sigma-Aldrich, St. Louis, MO, USA , and blocked with 5% BSA for 1 h. Following incubation with a mouse monoclonal antibody against c-myc-tag Sigma-Aldrich, St. Louis, MO, USA, Sigma-Aldrich Cat# M5546 , IFITM3, lysosome-associated membrane glycoprotein 1 LAMP1, Cell Signaling Technology, Danvers, MA, USA , or HTNV NP at 37°C for 2 h, the cells were washed and incubated with anti-rabbit Ig conjugated to Alexa 555 and anti-mouse Ig conjugated to Alexa 488 Abcam, Cambridge, MA, USA secondary antibodies at room temperature for 1 h. The nuclei were counterstained with DAPI. An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images.", "An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images. hTnV binding and entry assay Cells transduced with IFITM3 or the empty vector were detached and washed extensively with cold PBS. The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation.", "The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation. Part of cells were washed extensively with ice-cold PBS and harvested for binding assay. Another part of cells were switched to 37°C for 2 h to allow HTNV entry.", "Another part of cells were switched to 37°C for 2 h to allow HTNV entry. The HTNV that remained on the cell surface was removed by treatment with proteinase K 0.1 mg/ml, Thermo Scientific, Waltham, MA, USA . To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl.", "To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl. Adsorption of HTNV moi = 1 was performed at 4°C for 1 h. The cells were then washed, and fusion of the virus with the plasma membrane was triggered by incubation in low pH medium 20 mM sodium succinate, pH = 5.5 for 10 min at 37°C. Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl .", "Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl . . qPCR analysis of the HTNV S segment was conducted to evaluate the influence of IFITMs on HTNV cell adhesion and HTNV entry. All data were expressed as the mean ± SEM.", "All data were expressed as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 GraphPad Software, La Jolla, CA, USA . For association analysis of the rs12252 allele and genotype, Fisher's exact test was used. Independent samples t-tests were used for normally distributed data.", "Independent samples t-tests were used for normally distributed data. Differences among groups were determined by one-way analysis of variance ANOVA with repeated measures, followed by Bonferroni's post hoc test. P < 0.05 was considered statistically significant.", "P < 0.05 was considered statistically significant. The iFiTM3 snP rs12252 c allele and cc genotype associated with severe hFrs Disease and a higher Plasma hTnV load To determine the clinical significance of IFITM3 SNP in HTNV infection, the relationship between rs12252 SNP and the severity of HFRS in 69 patients were examined. We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients.", "We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients. Then, we stratified these patients into mild and severe, based on the clinical assessment as described in Section \"Material and Methods. \" We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 .", "We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 . The frequency of rs12252 C in severe patients was also higher than those mild patients 68.29 vs. 46.43%, P = 0.013, Figures 1A,B; Table 2 . These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 .", "These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 . For genotypes, 43.90% of the severe patients carried the CC genotype, a significantly higher frequency than the control Han Chinese per 1,000 genomes sequence database 26.92% CC genotype, P = 0.03 as well as mildly infected patients 14.29%, P = 0.02, Figures 1A,B ; Table 2 . However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity.", "However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity. c The plasma HTNV load in CC genotype patients and CT/TT genotype patients, tested by qRCR analysis. Each symbol represents one individual patient.", "Each symbol represents one individual patient. Independent samples t-test was used to test the difference of HTNV load between groups. *P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population.", "*P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population. In addition, we also found that patients with CC genotype had higher plasma viral load in acute phase Figure 1C . These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS.", "These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS. Previous studies reveal that the truncated IFITM3 protein produced by SNP rs12252 C allele Figure 2A , the missing part stands for the truncated 21 amino acids from N-terminal of IFITM3, the intramembrane helix, and transmembrane helix was presented as boxes leads to an impaired anti-influenza activity . .", ". To test the functional significance of this polymorphism in HTNV infection, we transfected the majority T or minority C variant IFITM3 alleles that produce full-length or N-terminally truncated NΔ21 proteins Figure 2A with c-myc-tag to HUVEC and A549 cell using lentivirus vectors Figure 2B . Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material .", "Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material . Indeed, compared with the mock empty vector -infected control, the NΔ21 protein almost lost the ability to inhibit HTNV infection in both HUVEC and A549 cells Figures 2C,D ; Figure S3 in Supplementary Material . To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material .", "To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material . While we observed only a moderate upregulation of IFITM1, 2, and 3 mRNA and protein in HUVECs after more than 24 h postinfection; IFITM1, 2, and 3 mRNA, however, were only transiently upregulated in A549 cells and caused no significant change in protein level. We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually.", "We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually. The effect of siRNAs on the expression of target IFITMs was tested by qPCR in HUVECs Figure S2 in Supplementary Material , and the effect of the best oligo against each IFITMs IFITM1C, IFITM2A, IFITM3B was tested by Western blot in A549 Figure 4A and HUVEC cells Figure 4B . To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h .", "To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h . The cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were significantly suppressed in both HUVEC and A549 cells in response to IFN-α2a treatment. Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells.", "Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells. Knockdown of IFITM1 also partially restored the HTNV level in A549 cells Figures 4C,D . These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection.", "These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection. c-myc-tagged IFITM1, 2, and 3 were expressed in both HUVEC and A549 cells Figure 5A , and the cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were suppressed by IFITM3 overexpression in HUVEC cells Figures 5B-D . They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D .", "They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D . The inhibitory effect of IFITM3 was further confirmed by immunofluorescence analysis of HTNV NP Figure S3 in Supplementary Material . These results were in accordance with the above-described RNAi results.", "These results were in accordance with the above-described RNAi results. To determine whether IFITM3 inhibited HTNV binding or entry, HUVEC and A549 cells were incubated with HTNV moi = 1 at 4°C for 1 h, unbound virus was washed away, and HTNV RNA collected at this time point represents HTNV bound to the cell surface. After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus.", "After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus. As a positive control for inhibition of virus entry, we incubated a parallel group of cells with HTNV at pH = 5.5 as described in Section \"Materials and Methods.\" Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B .", "Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B . iFiTM3 Was Partially localized to laMP1 + late endosomes in the host cells To elucidate the mechanism of IFITM3 function, we investigated the subcellular localization of IFTIM3 in the host cells. IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C .", "IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C . The co-localization of IFITM3 and LAMP1 + late endosomes had also been found in A549 cells . . Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry .", "Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry . , this result provides an evidence for the anti-HTNV mechanism of IFITM3. LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies.", "LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies. We tested the change of previously reported regulators of IFITMs, such as NRAV, NRIR, and miR-130a after HTNV infection, among which NRIR was the only changed one downregulated after HTNV infection Figure 7A ; Figure S4 in Supplementary Material in HUVEC. However, the expression of NRIR was unchanged in A549 cells.", "However, the expression of NRIR was unchanged in A549 cells. We overexpressed NRIR in HUVEC and A549 cells using the pcDNA3.1 vector Figure 7B . Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E .", "Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E . These data suggest that lncRNA NRIR is a negative regulator of IFITM3 transcription. Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae.", "Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae. It causes HFRS, which is an important threat to public health worldwide. It is also a potential weapon for biological terrorism. Reservoir animals, usually rodents, are asymptomatic during persistent infection.", "Reservoir animals, usually rodents, are asymptomatic during persistent infection. Unlike in rodents, Hantavirus infection leads to HFRS and Hantavirus pulmonary syndrome HPS in humans . . The major clinical characteristics of HFRS include fever, hemorrhage, hypotension, and renal injury . , causing severe manifestations and death in some cases.", ", causing severe manifestations and death in some cases. The current standard of care for HFRS relies on symptomatic and supportive treatment. It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS ..", "It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS .. However, no direct antiviral medications are currently available for this illness. Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases.", "Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases. It has been reported that HTNV infection induces a late type I interferon response . . However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified.", "However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified. Therefore, identification of ISGs that are effective against HTNV is an attractive strategy to identify novel therapeutic targets. In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese.", "In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese. The rs12252 C allele and CC genotype are also found to be associated with higher plasma viral load in the early stage of HFRS. We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity.", "We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity. Interestingly, IFITM3 is found to restrict HTNV infection with a mechanism of cellular entry inhibition. Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry.", "Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry. In addition, we find that HTNV infection downregulated lncRNA NRIR 48 h post infection, which negatively regulates the transcription of IFITM3. Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS.", "Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. The antiviral properties of IFITM proteins were identified in 2009 in an RNAi screen for host factors that influence influenza virus replication . .", ". IFITM1, 2, and 3 have been demonstrated to possess antiviral activity in several studies. Everitt et al. demonstrated that the severity of influenza virus infection was greatly increased in IFITM3-knockout mice compared with wild-type animals . .", ". Different IFITM members have also been confirmed to inhibit the cellular entry of multiple virus families including filoviruses, rhabdoviruses, and flaviviruses 7, . . . 30 . For example, HIV-1 and HCV infection are inhibited by IFITM1 . . . . . It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry .", "It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry . . Recent studies suggested that the cellular location of different IFITMs may influence the range of viruses restricted by each protein . . IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles .", "IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles . , whereas IFITM3 confines influenza virus in acidified endosomal compartments . .", ", whereas IFITM3 confines influenza virus in acidified endosomal compartments . . Notably, retrovirus subvirus particles ISVPs , which do not require endosomal acidification for entry, are not inhibited by IFITM3 expression, suggesting that IFITM3 may function at the stage of endosomal entry . .", ". Studies utilizing cell-cell fusion assays have suggested that IFITM3 blocks the entry of enveloped virus by preventing the fusion of the viral membrane with a limiting membrane of the host cell, either the plasma membrane and/or the endosomal membranes. The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes .", "The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes . . In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells.", "In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells. Overexpression of IFITM3 inhibited HTNV infection to HUVEC and A549 cells. IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells.", "IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells. All these results suggest that IFITM3 is an important control factor under natural infection of HTNV. Our results also demonstrate that the effectiveness of IFITM3 is cell type-independent, which is in accordance with the results from similar viruses, such as RVFV . .", ". Binding and entry assays, conducted by controlling the temperature and pH, showed that IFITM3 did not significantly influence HTNV binding but inhibited HTNV entry into HUVEC and A549 cells. Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry.", "Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry. However, we failed in tracking the transportation of HTNV in infected cells possibly due to the lack of fluorescence-labeled virus. In addition, IFITM1 also suppressed HTNV infection in A549 cells.", "In addition, IFITM1 also suppressed HTNV infection in A549 cells. The mechanism underlying anti-HTNV effect of IFITM1 remains undetermined and deserves to be further explored. According to a recent study on the three-dimensional structure of IFITM3, there is a C-terminal transmembrane α-helix and a two-N-terminal intramembrane α-helices shown in Figure 2A as black boxes in IFITM3 . .", ". There are two splice variants that differ by the presence or absence of the first N-terminal 21 amino acids deleted part, shown in Figure 2A as red dotted line . Several SNPs including 13 non-synonymous, 13 synonymous, 1 in-frame stop, and 1 splice site acceptoraltering have been reported in the translated IFITM3 sequence . .", ". Among them, the rare SNP rs12252C allele of IFITM3 truncates the protein as described above, leading to a reduced inhibition of influenza virus infection in A549 cells . . We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro.", "We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro. In Northern European patients hospitalized with seasonal influenza or pandemic influenza A virus, increased homozygosity of the minor C allele of SNP rs12252 in IFITM3 was observed . .", ". In Chinese patients infected with influenza A H1N1 virus, there was also an increased frequency of the C allele and CC genotype of SNP rs12252 . . In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients.", "In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients. Patients carrying the CC genotype also had higher plasma viral loads compared with those with the CT/TT genotype. Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo.", "Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo. We speculate that the much higher level of CC allele at healthy population of Han Chinese compared with Caucasians may place the Chinese at a higher risk for developing severe illness upon HTNV infection, which needs further investigation. LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity.", "LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity. For example, lncRNA NEAT1 is reported to be upregulated by influenza virus or PolyI:C stimulation, which promotes IL-8 expression . .", ". lncRNA NRAV has been shown to negatively regulate the initial transcription of IFITM3 and Mx1 by affecting the histone modification of these genes . . lncRNA NRIR is a non-coding ISG, which has been reported to negatively regulate IFITM1 and Mx1 expression in HCV infection . . Mir-130a was also reported as a regulator of IFITM1 . .", ". Mir-130a was also reported as a regulator of IFITM1 . . In this analysis, lncRNA NRIR was downregulated in HUVECs after HTNV infection for 48 h, overexpression of NRIR negatively regulates the initial transcription of IFITM3, evidenced by the decreased pre-mRNA as well as mRNA levels. NRIR overexpression also facilitated HTNV infection.", "NRIR overexpression also facilitated HTNV infection. These results indicate that the downregulation of NRIR after HTNV infection is possibly involved in the activation of innate immune responses against HTNV infection. We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study.", "We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study. Another lncRNA that can regulate IFITM3, i.e., NRAV NR_038854 , remained unchanged after HTNV infection Figures S4A,B in Supplementary Material . Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material .", "Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material . In conclusion, this study revealed a critical role for IFITM3 in HTNV infection. We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells.", "We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells. In addition, we discovered that the IFITM3 SNP rs12252 C allele and CC genotype correlates with the plasma HTNV load and the severity of HFRS; and the rs12252 C allele produces a truncated IFITM3 protein NΔ21 that attenuates its anti-HTNV function. These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease.", "These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease. aUThOr cOnTribUTiOns ZX-y, BP-y, YC-t, and MH-w performed the experiments; WP-z, BX-f, LY-f, ZY, and JZ-s designed the research; HC-x, YW, and WX analyzed the data; TK and ZC-m provided clinical data; ZX-y and BP-y wrote the paper." ]

[ "Hantaan virus HTNV causes hemorrhagic fever with renal syndrome HFRS . Previous studies have identified interferon-induced transmembrane proteins IFITMs as an interferon-stimulated gene family. However, the role of IFITMs in HTNV infection is unclear.", "However, the role of IFITMs in HTNV infection is unclear. In this study, we observed that IFITM3 single nucleotide polymorphisms SNP rs12252 C allele and CC genotype associated with the disease severity and HTNV load in the plasma of HFRS patients. In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity.", "In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity. We also proved that IFITM3 was able to inhibit HTNV infection in both HUVEC and A549 cells by overexpression and RNAi assays, likely via a mechanism of inhibiting virus entry demonstrated by binding and entry assay. Localization of IFITM3 in late endosomes was also observed.", "Localization of IFITM3 in late endosomes was also observed. In addition, we demonstrated that the transcription of IFITM3 is negatively regulated by an lncRNA negative regulator of interferon response NRIR . Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS.", "Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS. Text: associates with the severity of disease, indicating the importance of viremia in the pathogenesis of HFRS . .", ". Therefore, further studies of host factors limiting HTNV infection and influencing antiviral response as well as disease progression are clinically significant and timely. The human family of interferon-induced transmembrane proteins IFITMs was discovered 25 years ago to consist of interferon-stimulated genes ISGs . .", ". This family includes five members, namely, IFITM1, 2, 3, 5, and 10, among which IFITM1, 2, and 3 possess antiviral activity . . Different IFITM proteins have different antiviral spectrum . . For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice .", "For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice . , and it also inhibits multiple viruses, including filoviruses, rhabdoviruses, flaviviruses, and even Ebola and Zika virus . . . . . . The antiviral mechanism of IFITM3 is thought to be the restriction of viral entry into cells . .", ". Single nucleotide polymorphisms SNPs are single nucleotide variations in a genetic sequence that occur at an appreciable frequency in the population. Several SNPs has been identified in IFITM3, among which the rs12252 site with C allele results in a N-terminal truncation of IFITM3 protein, leading to impaired inhibition of influenza virus in vitro . .", ". Notably, the frequencies of rs12252 C allele and CC genotype correlate with disease severity in patients infected with influenza virus . . HTNV has been shown to induce a type I interferon response though in later time postinfection . . While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection .", "While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection . , however, the effect of IFITMs on HTNV infection in human cell lines and its role in HFRS still remain unknown. LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators.", "LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators. Some lncRNAs have been shown to play a role in innate immunity . . Among them, negative regulator of interferon response NRIR lncRNA NRIR, also known as lncRNA-CMPK2 is a non-coding ISG that negatively regulates IFITM1 and Mx1 expression in HCV infection . .", ". Notably, IFITM3 is largely homologous to IFITM1, but the role of NRIR in the regulation of IFITM3 in HTNV infection remains unclear. In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans.", "In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans. We provide primary evidence suggesting that IFITM3, regulated by NRIR, can inhibit HTNV infection and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection.", "This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection. This study was conducted in accordance with the recommendations of the biomedical research guidelines involving human participants established by the National Health and Family Planning Commission of China. The Institutional Ethics Committee of Tangdu Hospital approved this study.", "The Institutional Ethics Committee of Tangdu Hospital approved this study. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained.", "Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained. Sixty-nine HFRS patients admitted into the Department of Infectious Diseases, Tangdu Hospital between October 2014 and March 2016 were enrolled in this study. All patients were Han Chinese.", "All patients were Han Chinese. The diagnosis of HFRS was made based on typical symptoms and signs as well as positive IgM and IgG antibodies against HTNV in the serum assessed by enzyme linked immunosorbent assay ELISA in our department. The classification of HFRS severity and the exclusion criteria were described as follows .", "The classification of HFRS severity and the exclusion criteria were described as follows . : white blood cells WBC , platelets PLT , blood urea nitrogen BUN , serum creatinine Scr , and heteromorphic lymphocytes that were tested by the Department of Clinical Laboratory shown in Table 1 . According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described .", "According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described . : . mild patients were identified with mild renal failure without an obvious oliguric stage; .", "mild patients were identified with mild renal failure without an obvious oliguric stage; . moderate patients were those with obvious symptoms of uremia, effusion bulbar conjunctiva , hemorrhage skin and mucous membrane , and renal failure with a typical oliguric stage; . severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and .", "severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and . critical patients exhibited ≥1 of the following signs during the illness: refractory shock, visceral hemorrhage, heart failure, pulmonary edema, brain edema, severe secondary infection, and severe renal failure with oliguria urine output, 50-500 ml/day for >5 days, anuria urine output, <50 ml/day for >2 days, or a BUN level of >42.84 mmol/l. Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group.", "Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group. The exclusion criteria for this study were patients with: . any other kidney disease, . diabetes mellitus, . autoimmune disease, . hematological disease, .", "diabetes mellitus, . autoimmune disease, . hematological disease, . cardiovascular disease, . viral hepatitis types A, B, C, D, or E , or . any other liver disease. In addition, no patients received corticosteroids or other immunomodulatory drugs during the study period . .", ". Genomic DNA was extracted from the peripheral blood of patients using the PureGene DNA Isolation kit Gentra Systems, Minneapolis, MN, USA . The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ .", "The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ . The PCR products were purified and sequenced using an Applied Biosystems 3730xl DNA Analyzer Thermo Scientific, Waltham, MA, USA . The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods .", "The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods . . Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA .", "Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA . The SuperScript III Platinum One-Step Quantitative RT-PCR System kit Invitrogen, Carlsbad, CA, USA was employed for the real-time RT-PCR assay. The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′.", "The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′. The synthetic S segment of the HTNV standard strain 76-118 RNA transcript was used as the quantitative calibrator. The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators.", "The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators. For each experiment, one aliquot of calibrated 76-118 standard was extracted in parallel with the clinical samples and serially 10-fold diluted with concentrations ranging from 10.56 to 2.56 log10 copies/ml. PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C.", "PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C. Fluorescence was read during the 72°C step of the final segment of every cycling program. HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator.", "HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator. A549 cells ATCC Cat# CRM-CCL-185, RRID:CVCL_0023 were grown in our laboratory in DMEM with 10% FBS Thermo Scientific, Waltham, MA, USA in a 5% CO2 incubator. Cells were used within passage 10 after primary culture.", "Cells were used within passage 10 after primary culture. HTNV strain 76-118 was cultured in Vero E6 cells ATCC Cat# CRL-1586, RRID:CVCL_0574 in our laboratory and titrated using an immunofluorescence staining assay for HTNV nucleocapsid protein NP as previously described . . The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method.", ". The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method. The recombinant human IFN-α2a was obtained from PBL Interferon Source Piscataway, NJ, USA and dissolved in the buffer provided by the manufacturer composition not disclosed . HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins.", "HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins. Subsequently, the virus solution was removed and fresh medium added to the cell culture. Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10.", "Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10. Puromycin 2 μg/ ml for HUVEC and 6 μg/ml for A549 cells was used to create cell lines stably expressing IFITMs. Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA .", "Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA . SiRNAs were purchased from Origene Rockville, MD, USA , and the sequences were not disclosed. Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA .", "Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA . Quantitative realtime PCR qPCR was performed using SYBR Premix Ex Taq II Takara Biotechnology Co., Dalian, China with a Bio-Rad iQ5 cycler Bio-Rad, Hercules, CA, USA . β-actin was used as the reference gene.", "β-actin was used as the reference gene. The primers Sangon Biotech, Shanghai, China were as follows: IFITM1 forward, 5′-ACTCCGTGAAGTCTAGGGACA-3′ and reverse, 5′-TGTCACAGAGCCGAATACCAG-3′ ; IFITM2 forward, 5′-ATCCCGGTAACCCGATCAC-3′ and reverse, 5′-CTTCCTGTCCCTAGACTTCAC-3′ ; IFITM3 forward, 5′-GGTCTTCGCTGGACACCAT-3′ and reverse, 5′-TGTCCCTAGACTTCACGGAGTA-3′ ; IFITM3 pre-mRNA forward, 5′-CATAGCACGCGGCTCT CAG-3′ and reverse, 5′-CGTCGCCAACCATCTTCCTG-3′ ; HTNV S segment forward, 5′-GCCTGGAGACCATCTGA AAG-3′ and reverse, 5′-AGTATCGGGACGACAAAGGA-3′ ; β-actin forward, 5′-GCTACGTCGCCCTGGACTTC-3′ and reverse, 5′-GTCATAGTCCGCCTAGAAGC-3′ ; NRIR forward, 5′-ATGGTTTTCTGGTGCCTTG-3′ and reverse, 5′-GGAGGTTAGAGGTGTCTGCTG-3′ ; NRAV forward, 5′-TCACTACTGCCCCAGGATCA-3′ and reverse, 5′-GGTGGTCACAGGACTCATGG-3′ . For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA .", "For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . MiR-130a level was determined using the gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control .", "U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control . . Because the pre-mRNA levels can represent the initial transcription rate . , the primers used to detect the pre-mRNA of IFITM3 were designed targeting the intron of IFITM3 as previously described . . IFITM3 has two exons and one intron.", ". IFITM3 has two exons and one intron. For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon.", "For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon. For qPCR of IFITM3 mRNA, the forward primers were positioned in the first exon, and the reverse primer was positioned at the beginning of the second exon . .", ". Because the basal expression of IFITM3 is low in A549 cells, we detected IFITM3 mRNA and pre-mRNA in A549 cells following IFN-α2a treatment 20 IU/ml for 12 h after the overexpression of NRIR. Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA .", "Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA . Equal amounts of protein 20 μg protein/lane were electrophoresed on a 10%-SDS-polyacrylamide gel and electrophoretically transferred to a polyvinylidene difluoride membrane Millipore, Billerica, MA, USA . After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C.", "After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C. The membranes were then washed and incubated with HRP-conjugated IgG antibody Cell Signaling Technology, Danvers, MA, USA for 1 h at room temperature. The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film.", "The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film. The blot densities were analyzed using the Quantity One software Bio-Rad, Hercules, CA, USA . In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS.", "In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS. Protease inhibitor cocktail Roche, Basel, Switzerland was added before use. The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 .", "The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 . At the indicated times post-HTNV infection, the cells were fixed with 4% PFA, incubated with 0.3% Triton X-100 Sigma-Aldrich, St. Louis, MO, USA , and blocked with 5% BSA for 1 h. Following incubation with a mouse monoclonal antibody against c-myc-tag Sigma-Aldrich, St. Louis, MO, USA, Sigma-Aldrich Cat# M5546 , IFITM3, lysosome-associated membrane glycoprotein 1 LAMP1, Cell Signaling Technology, Danvers, MA, USA , or HTNV NP at 37°C for 2 h, the cells were washed and incubated with anti-rabbit Ig conjugated to Alexa 555 and anti-mouse Ig conjugated to Alexa 488 Abcam, Cambridge, MA, USA secondary antibodies at room temperature for 1 h. The nuclei were counterstained with DAPI. An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images.", "An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images. hTnV binding and entry assay Cells transduced with IFITM3 or the empty vector were detached and washed extensively with cold PBS. The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation.", "The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation. Part of cells were washed extensively with ice-cold PBS and harvested for binding assay. Another part of cells were switched to 37°C for 2 h to allow HTNV entry.", "Another part of cells were switched to 37°C for 2 h to allow HTNV entry. The HTNV that remained on the cell surface was removed by treatment with proteinase K 0.1 mg/ml, Thermo Scientific, Waltham, MA, USA . To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl.", "To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl. Adsorption of HTNV moi = 1 was performed at 4°C for 1 h. The cells were then washed, and fusion of the virus with the plasma membrane was triggered by incubation in low pH medium 20 mM sodium succinate, pH = 5.5 for 10 min at 37°C. Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl .", "Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl . . qPCR analysis of the HTNV S segment was conducted to evaluate the influence of IFITMs on HTNV cell adhesion and HTNV entry. All data were expressed as the mean ± SEM.", "All data were expressed as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 GraphPad Software, La Jolla, CA, USA . For association analysis of the rs12252 allele and genotype, Fisher's exact test was used. Independent samples t-tests were used for normally distributed data.", "Independent samples t-tests were used for normally distributed data. Differences among groups were determined by one-way analysis of variance ANOVA with repeated measures, followed by Bonferroni's post hoc test. P < 0.05 was considered statistically significant.", "P < 0.05 was considered statistically significant. The iFiTM3 snP rs12252 c allele and cc genotype associated with severe hFrs Disease and a higher Plasma hTnV load To determine the clinical significance of IFITM3 SNP in HTNV infection, the relationship between rs12252 SNP and the severity of HFRS in 69 patients were examined. We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients.", "We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients. Then, we stratified these patients into mild and severe, based on the clinical assessment as described in Section \"Material and Methods. \" We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 .", "We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 . The frequency of rs12252 C in severe patients was also higher than those mild patients 68.29 vs. 46.43%, P = 0.013, Figures 1A,B; Table 2 . These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 .", "These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 . For genotypes, 43.90% of the severe patients carried the CC genotype, a significantly higher frequency than the control Han Chinese per 1,000 genomes sequence database 26.92% CC genotype, P = 0.03 as well as mildly infected patients 14.29%, P = 0.02, Figures 1A,B ; Table 2 . However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity.", "However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity. c The plasma HTNV load in CC genotype patients and CT/TT genotype patients, tested by qRCR analysis. Each symbol represents one individual patient.", "Each symbol represents one individual patient. Independent samples t-test was used to test the difference of HTNV load between groups. *P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population.", "*P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population. In addition, we also found that patients with CC genotype had higher plasma viral load in acute phase Figure 1C . These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS.", "These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS. Previous studies reveal that the truncated IFITM3 protein produced by SNP rs12252 C allele Figure 2A , the missing part stands for the truncated 21 amino acids from N-terminal of IFITM3, the intramembrane helix, and transmembrane helix was presented as boxes leads to an impaired anti-influenza activity . .", ". To test the functional significance of this polymorphism in HTNV infection, we transfected the majority T or minority C variant IFITM3 alleles that produce full-length or N-terminally truncated NΔ21 proteins Figure 2A with c-myc-tag to HUVEC and A549 cell using lentivirus vectors Figure 2B . Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material .", "Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material . Indeed, compared with the mock empty vector -infected control, the NΔ21 protein almost lost the ability to inhibit HTNV infection in both HUVEC and A549 cells Figures 2C,D ; Figure S3 in Supplementary Material . To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material .", "To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material . While we observed only a moderate upregulation of IFITM1, 2, and 3 mRNA and protein in HUVECs after more than 24 h postinfection; IFITM1, 2, and 3 mRNA, however, were only transiently upregulated in A549 cells and caused no significant change in protein level. We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually.", "We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually. The effect of siRNAs on the expression of target IFITMs was tested by qPCR in HUVECs Figure S2 in Supplementary Material , and the effect of the best oligo against each IFITMs IFITM1C, IFITM2A, IFITM3B was tested by Western blot in A549 Figure 4A and HUVEC cells Figure 4B . To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h .", "To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h . The cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were significantly suppressed in both HUVEC and A549 cells in response to IFN-α2a treatment. Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells.", "Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells. Knockdown of IFITM1 also partially restored the HTNV level in A549 cells Figures 4C,D . These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection.", "These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection. c-myc-tagged IFITM1, 2, and 3 were expressed in both HUVEC and A549 cells Figure 5A , and the cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were suppressed by IFITM3 overexpression in HUVEC cells Figures 5B-D . They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D .", "They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D . The inhibitory effect of IFITM3 was further confirmed by immunofluorescence analysis of HTNV NP Figure S3 in Supplementary Material . These results were in accordance with the above-described RNAi results.", "These results were in accordance with the above-described RNAi results. To determine whether IFITM3 inhibited HTNV binding or entry, HUVEC and A549 cells were incubated with HTNV moi = 1 at 4°C for 1 h, unbound virus was washed away, and HTNV RNA collected at this time point represents HTNV bound to the cell surface. After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus.", "After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus. As a positive control for inhibition of virus entry, we incubated a parallel group of cells with HTNV at pH = 5.5 as described in Section \"Materials and Methods.\" Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B .", "Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B . iFiTM3 Was Partially localized to laMP1 + late endosomes in the host cells To elucidate the mechanism of IFITM3 function, we investigated the subcellular localization of IFTIM3 in the host cells. IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C .", "IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C . The co-localization of IFITM3 and LAMP1 + late endosomes had also been found in A549 cells . . Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry .", "Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry . , this result provides an evidence for the anti-HTNV mechanism of IFITM3. LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies.", "LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies. We tested the change of previously reported regulators of IFITMs, such as NRAV, NRIR, and miR-130a after HTNV infection, among which NRIR was the only changed one downregulated after HTNV infection Figure 7A ; Figure S4 in Supplementary Material in HUVEC. However, the expression of NRIR was unchanged in A549 cells.", "However, the expression of NRIR was unchanged in A549 cells. We overexpressed NRIR in HUVEC and A549 cells using the pcDNA3.1 vector Figure 7B . Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E .", "Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E . These data suggest that lncRNA NRIR is a negative regulator of IFITM3 transcription. Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae.", "Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae. It causes HFRS, which is an important threat to public health worldwide. It is also a potential weapon for biological terrorism. Reservoir animals, usually rodents, are asymptomatic during persistent infection.", "Reservoir animals, usually rodents, are asymptomatic during persistent infection. Unlike in rodents, Hantavirus infection leads to HFRS and Hantavirus pulmonary syndrome HPS in humans . . The major clinical characteristics of HFRS include fever, hemorrhage, hypotension, and renal injury . , causing severe manifestations and death in some cases.", ", causing severe manifestations and death in some cases. The current standard of care for HFRS relies on symptomatic and supportive treatment. It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS ..", "It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS .. However, no direct antiviral medications are currently available for this illness. Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases.", "Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases. It has been reported that HTNV infection induces a late type I interferon response . . However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified.", "However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified. Therefore, identification of ISGs that are effective against HTNV is an attractive strategy to identify novel therapeutic targets. In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese.", "In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese. The rs12252 C allele and CC genotype are also found to be associated with higher plasma viral load in the early stage of HFRS. We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity.", "We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity. Interestingly, IFITM3 is found to restrict HTNV infection with a mechanism of cellular entry inhibition. Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry.", "Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry. In addition, we find that HTNV infection downregulated lncRNA NRIR 48 h post infection, which negatively regulates the transcription of IFITM3. Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS.", "Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. The antiviral properties of IFITM proteins were identified in 2009 in an RNAi screen for host factors that influence influenza virus replication . .", ". IFITM1, 2, and 3 have been demonstrated to possess antiviral activity in several studies. Everitt et al. demonstrated that the severity of influenza virus infection was greatly increased in IFITM3-knockout mice compared with wild-type animals . .", ". Different IFITM members have also been confirmed to inhibit the cellular entry of multiple virus families including filoviruses, rhabdoviruses, and flaviviruses 7, . . . 30 . For example, HIV-1 and HCV infection are inhibited by IFITM1 . . . . . It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry .", "It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry . . Recent studies suggested that the cellular location of different IFITMs may influence the range of viruses restricted by each protein . . IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles .", "IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles . , whereas IFITM3 confines influenza virus in acidified endosomal compartments . .", ", whereas IFITM3 confines influenza virus in acidified endosomal compartments . . Notably, retrovirus subvirus particles ISVPs , which do not require endosomal acidification for entry, are not inhibited by IFITM3 expression, suggesting that IFITM3 may function at the stage of endosomal entry . .", ". Studies utilizing cell-cell fusion assays have suggested that IFITM3 blocks the entry of enveloped virus by preventing the fusion of the viral membrane with a limiting membrane of the host cell, either the plasma membrane and/or the endosomal membranes. The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes .", "The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes . . In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells.", "In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells. Overexpression of IFITM3 inhibited HTNV infection to HUVEC and A549 cells. IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells.", "IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells. All these results suggest that IFITM3 is an important control factor under natural infection of HTNV. Our results also demonstrate that the effectiveness of IFITM3 is cell type-independent, which is in accordance with the results from similar viruses, such as RVFV . .", ". Binding and entry assays, conducted by controlling the temperature and pH, showed that IFITM3 did not significantly influence HTNV binding but inhibited HTNV entry into HUVEC and A549 cells. Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry.", "Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry. However, we failed in tracking the transportation of HTNV in infected cells possibly due to the lack of fluorescence-labeled virus. In addition, IFITM1 also suppressed HTNV infection in A549 cells.", "In addition, IFITM1 also suppressed HTNV infection in A549 cells. The mechanism underlying anti-HTNV effect of IFITM1 remains undetermined and deserves to be further explored. According to a recent study on the three-dimensional structure of IFITM3, there is a C-terminal transmembrane α-helix and a two-N-terminal intramembrane α-helices shown in Figure 2A as black boxes in IFITM3 . .", ". There are two splice variants that differ by the presence or absence of the first N-terminal 21 amino acids deleted part, shown in Figure 2A as red dotted line . Several SNPs including 13 non-synonymous, 13 synonymous, 1 in-frame stop, and 1 splice site acceptoraltering have been reported in the translated IFITM3 sequence . .", ". Among them, the rare SNP rs12252C allele of IFITM3 truncates the protein as described above, leading to a reduced inhibition of influenza virus infection in A549 cells . . We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro.", "We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro. In Northern European patients hospitalized with seasonal influenza or pandemic influenza A virus, increased homozygosity of the minor C allele of SNP rs12252 in IFITM3 was observed . .", ". In Chinese patients infected with influenza A H1N1 virus, there was also an increased frequency of the C allele and CC genotype of SNP rs12252 . . In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients.", "In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients. Patients carrying the CC genotype also had higher plasma viral loads compared with those with the CT/TT genotype. Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo.", "Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo. We speculate that the much higher level of CC allele at healthy population of Han Chinese compared with Caucasians may place the Chinese at a higher risk for developing severe illness upon HTNV infection, which needs further investigation. LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity.", "LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity. For example, lncRNA NEAT1 is reported to be upregulated by influenza virus or PolyI:C stimulation, which promotes IL-8 expression . .", ". lncRNA NRAV has been shown to negatively regulate the initial transcription of IFITM3 and Mx1 by affecting the histone modification of these genes . . lncRNA NRIR is a non-coding ISG, which has been reported to negatively regulate IFITM1 and Mx1 expression in HCV infection . . Mir-130a was also reported as a regulator of IFITM1 . .", ". Mir-130a was also reported as a regulator of IFITM1 . . In this analysis, lncRNA NRIR was downregulated in HUVECs after HTNV infection for 48 h, overexpression of NRIR negatively regulates the initial transcription of IFITM3, evidenced by the decreased pre-mRNA as well as mRNA levels. NRIR overexpression also facilitated HTNV infection.", "NRIR overexpression also facilitated HTNV infection. These results indicate that the downregulation of NRIR after HTNV infection is possibly involved in the activation of innate immune responses against HTNV infection. We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study.", "We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study. Another lncRNA that can regulate IFITM3, i.e., NRAV NR_038854 , remained unchanged after HTNV infection Figures S4A,B in Supplementary Material . Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material .", "Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material . In conclusion, this study revealed a critical role for IFITM3 in HTNV infection. We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells.", "We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells. In addition, we discovered that the IFITM3 SNP rs12252 C allele and CC genotype correlates with the plasma HTNV load and the severity of HFRS; and the rs12252 C allele produces a truncated IFITM3 protein NΔ21 that attenuates its anti-HTNV function. These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease.", "These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease. aUThOr cOnTribUTiOns ZX-y, BP-y, YC-t, and MH-w performed the experiments; WP-z, BX-f, LY-f, ZY, and JZ-s designed the research; HC-x, YW, and WX analyzed the data; TK and ZC-m provided clinical data; ZX-y and BP-y wrote the paper." ]

[ "Hantaan virus HTNV causes hemorrhagic fever with renal syndrome HFRS . Previous studies have identified interferon-induced transmembrane proteins IFITMs as an interferon-stimulated gene family. However, the role of IFITMs in HTNV infection is unclear.", "However, the role of IFITMs in HTNV infection is unclear. In this study, we observed that IFITM3 single nucleotide polymorphisms SNP rs12252 C allele and CC genotype associated with the disease severity and HTNV load in the plasma of HFRS patients. In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity.", "In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity. We also proved that IFITM3 was able to inhibit HTNV infection in both HUVEC and A549 cells by overexpression and RNAi assays, likely via a mechanism of inhibiting virus entry demonstrated by binding and entry assay. Localization of IFITM3 in late endosomes was also observed.", "Localization of IFITM3 in late endosomes was also observed. In addition, we demonstrated that the transcription of IFITM3 is negatively regulated by an lncRNA negative regulator of interferon response NRIR . Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS.", "Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS. Text: associates with the severity of disease, indicating the importance of viremia in the pathogenesis of HFRS . .", ". Therefore, further studies of host factors limiting HTNV infection and influencing antiviral response as well as disease progression are clinically significant and timely. The human family of interferon-induced transmembrane proteins IFITMs was discovered 25 years ago to consist of interferon-stimulated genes ISGs . .", ". This family includes five members, namely, IFITM1, 2, 3, 5, and 10, among which IFITM1, 2, and 3 possess antiviral activity . . Different IFITM proteins have different antiviral spectrum . . For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice .", "For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice . , and it also inhibits multiple viruses, including filoviruses, rhabdoviruses, flaviviruses, and even Ebola and Zika virus . . . . . . The antiviral mechanism of IFITM3 is thought to be the restriction of viral entry into cells . .", ". Single nucleotide polymorphisms SNPs are single nucleotide variations in a genetic sequence that occur at an appreciable frequency in the population. Several SNPs has been identified in IFITM3, among which the rs12252 site with C allele results in a N-terminal truncation of IFITM3 protein, leading to impaired inhibition of influenza virus in vitro . .", ". Notably, the frequencies of rs12252 C allele and CC genotype correlate with disease severity in patients infected with influenza virus . . HTNV has been shown to induce a type I interferon response though in later time postinfection . . While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection .", "While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection . , however, the effect of IFITMs on HTNV infection in human cell lines and its role in HFRS still remain unknown. LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators.", "LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators. Some lncRNAs have been shown to play a role in innate immunity . . Among them, negative regulator of interferon response NRIR lncRNA NRIR, also known as lncRNA-CMPK2 is a non-coding ISG that negatively regulates IFITM1 and Mx1 expression in HCV infection . .", ". Notably, IFITM3 is largely homologous to IFITM1, but the role of NRIR in the regulation of IFITM3 in HTNV infection remains unclear. In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans.", "In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans. We provide primary evidence suggesting that IFITM3, regulated by NRIR, can inhibit HTNV infection and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection.", "This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection. This study was conducted in accordance with the recommendations of the biomedical research guidelines involving human participants established by the National Health and Family Planning Commission of China. The Institutional Ethics Committee of Tangdu Hospital approved this study.", "The Institutional Ethics Committee of Tangdu Hospital approved this study. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained.", "Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained. Sixty-nine HFRS patients admitted into the Department of Infectious Diseases, Tangdu Hospital between October 2014 and March 2016 were enrolled in this study. All patients were Han Chinese.", "All patients were Han Chinese. The diagnosis of HFRS was made based on typical symptoms and signs as well as positive IgM and IgG antibodies against HTNV in the serum assessed by enzyme linked immunosorbent assay ELISA in our department. The classification of HFRS severity and the exclusion criteria were described as follows .", "The classification of HFRS severity and the exclusion criteria were described as follows . : white blood cells WBC , platelets PLT , blood urea nitrogen BUN , serum creatinine Scr , and heteromorphic lymphocytes that were tested by the Department of Clinical Laboratory shown in Table 1 . According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described .", "According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described . : . mild patients were identified with mild renal failure without an obvious oliguric stage; .", "mild patients were identified with mild renal failure without an obvious oliguric stage; . moderate patients were those with obvious symptoms of uremia, effusion bulbar conjunctiva , hemorrhage skin and mucous membrane , and renal failure with a typical oliguric stage; . severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and .", "severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and . critical patients exhibited ≥1 of the following signs during the illness: refractory shock, visceral hemorrhage, heart failure, pulmonary edema, brain edema, severe secondary infection, and severe renal failure with oliguria urine output, 50-500 ml/day for >5 days, anuria urine output, <50 ml/day for >2 days, or a BUN level of >42.84 mmol/l. Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group.", "Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group. The exclusion criteria for this study were patients with: . any other kidney disease, . diabetes mellitus, . autoimmune disease, . hematological disease, .", "diabetes mellitus, . autoimmune disease, . hematological disease, . cardiovascular disease, . viral hepatitis types A, B, C, D, or E , or . any other liver disease. In addition, no patients received corticosteroids or other immunomodulatory drugs during the study period . .", ". Genomic DNA was extracted from the peripheral blood of patients using the PureGene DNA Isolation kit Gentra Systems, Minneapolis, MN, USA . The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ .", "The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ . The PCR products were purified and sequenced using an Applied Biosystems 3730xl DNA Analyzer Thermo Scientific, Waltham, MA, USA . The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods .", "The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods . . Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA .", "Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA . The SuperScript III Platinum One-Step Quantitative RT-PCR System kit Invitrogen, Carlsbad, CA, USA was employed for the real-time RT-PCR assay. The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′.", "The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′. The synthetic S segment of the HTNV standard strain 76-118 RNA transcript was used as the quantitative calibrator. The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators.", "The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators. For each experiment, one aliquot of calibrated 76-118 standard was extracted in parallel with the clinical samples and serially 10-fold diluted with concentrations ranging from 10.56 to 2.56 log10 copies/ml. PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C.", "PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C. Fluorescence was read during the 72°C step of the final segment of every cycling program. HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator.", "HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator. A549 cells ATCC Cat# CRM-CCL-185, RRID:CVCL_0023 were grown in our laboratory in DMEM with 10% FBS Thermo Scientific, Waltham, MA, USA in a 5% CO2 incubator. Cells were used within passage 10 after primary culture.", "Cells were used within passage 10 after primary culture. HTNV strain 76-118 was cultured in Vero E6 cells ATCC Cat# CRL-1586, RRID:CVCL_0574 in our laboratory and titrated using an immunofluorescence staining assay for HTNV nucleocapsid protein NP as previously described . . The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method.", ". The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method. The recombinant human IFN-α2a was obtained from PBL Interferon Source Piscataway, NJ, USA and dissolved in the buffer provided by the manufacturer composition not disclosed . HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins.", "HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins. Subsequently, the virus solution was removed and fresh medium added to the cell culture. Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10.", "Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10. Puromycin 2 μg/ ml for HUVEC and 6 μg/ml for A549 cells was used to create cell lines stably expressing IFITMs. Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA .", "Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA . SiRNAs were purchased from Origene Rockville, MD, USA , and the sequences were not disclosed. Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA .", "Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA . Quantitative realtime PCR qPCR was performed using SYBR Premix Ex Taq II Takara Biotechnology Co., Dalian, China with a Bio-Rad iQ5 cycler Bio-Rad, Hercules, CA, USA . β-actin was used as the reference gene.", "β-actin was used as the reference gene. The primers Sangon Biotech, Shanghai, China were as follows: IFITM1 forward, 5′-ACTCCGTGAAGTCTAGGGACA-3′ and reverse, 5′-TGTCACAGAGCCGAATACCAG-3′ ; IFITM2 forward, 5′-ATCCCGGTAACCCGATCAC-3′ and reverse, 5′-CTTCCTGTCCCTAGACTTCAC-3′ ; IFITM3 forward, 5′-GGTCTTCGCTGGACACCAT-3′ and reverse, 5′-TGTCCCTAGACTTCACGGAGTA-3′ ; IFITM3 pre-mRNA forward, 5′-CATAGCACGCGGCTCT CAG-3′ and reverse, 5′-CGTCGCCAACCATCTTCCTG-3′ ; HTNV S segment forward, 5′-GCCTGGAGACCATCTGA AAG-3′ and reverse, 5′-AGTATCGGGACGACAAAGGA-3′ ; β-actin forward, 5′-GCTACGTCGCCCTGGACTTC-3′ and reverse, 5′-GTCATAGTCCGCCTAGAAGC-3′ ; NRIR forward, 5′-ATGGTTTTCTGGTGCCTTG-3′ and reverse, 5′-GGAGGTTAGAGGTGTCTGCTG-3′ ; NRAV forward, 5′-TCACTACTGCCCCAGGATCA-3′ and reverse, 5′-GGTGGTCACAGGACTCATGG-3′ . For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA .", "For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . MiR-130a level was determined using the gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control .", "U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control . . Because the pre-mRNA levels can represent the initial transcription rate . , the primers used to detect the pre-mRNA of IFITM3 were designed targeting the intron of IFITM3 as previously described . . IFITM3 has two exons and one intron.", ". IFITM3 has two exons and one intron. For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon.", "For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon. For qPCR of IFITM3 mRNA, the forward primers were positioned in the first exon, and the reverse primer was positioned at the beginning of the second exon . .", ". Because the basal expression of IFITM3 is low in A549 cells, we detected IFITM3 mRNA and pre-mRNA in A549 cells following IFN-α2a treatment 20 IU/ml for 12 h after the overexpression of NRIR. Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA .", "Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA . Equal amounts of protein 20 μg protein/lane were electrophoresed on a 10%-SDS-polyacrylamide gel and electrophoretically transferred to a polyvinylidene difluoride membrane Millipore, Billerica, MA, USA . After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C.", "After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C. The membranes were then washed and incubated with HRP-conjugated IgG antibody Cell Signaling Technology, Danvers, MA, USA for 1 h at room temperature. The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film.", "The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film. The blot densities were analyzed using the Quantity One software Bio-Rad, Hercules, CA, USA . In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS.", "In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS. Protease inhibitor cocktail Roche, Basel, Switzerland was added before use. The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 .", "The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 . At the indicated times post-HTNV infection, the cells were fixed with 4% PFA, incubated with 0.3% Triton X-100 Sigma-Aldrich, St. Louis, MO, USA , and blocked with 5% BSA for 1 h. Following incubation with a mouse monoclonal antibody against c-myc-tag Sigma-Aldrich, St. Louis, MO, USA, Sigma-Aldrich Cat# M5546 , IFITM3, lysosome-associated membrane glycoprotein 1 LAMP1, Cell Signaling Technology, Danvers, MA, USA , or HTNV NP at 37°C for 2 h, the cells were washed and incubated with anti-rabbit Ig conjugated to Alexa 555 and anti-mouse Ig conjugated to Alexa 488 Abcam, Cambridge, MA, USA secondary antibodies at room temperature for 1 h. The nuclei were counterstained with DAPI. An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images.", "An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images. hTnV binding and entry assay Cells transduced with IFITM3 or the empty vector were detached and washed extensively with cold PBS. The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation.", "The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation. Part of cells were washed extensively with ice-cold PBS and harvested for binding assay. Another part of cells were switched to 37°C for 2 h to allow HTNV entry.", "Another part of cells were switched to 37°C for 2 h to allow HTNV entry. The HTNV that remained on the cell surface was removed by treatment with proteinase K 0.1 mg/ml, Thermo Scientific, Waltham, MA, USA . To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl.", "To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl. Adsorption of HTNV moi = 1 was performed at 4°C for 1 h. The cells were then washed, and fusion of the virus with the plasma membrane was triggered by incubation in low pH medium 20 mM sodium succinate, pH = 5.5 for 10 min at 37°C. Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl .", "Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl . . qPCR analysis of the HTNV S segment was conducted to evaluate the influence of IFITMs on HTNV cell adhesion and HTNV entry. All data were expressed as the mean ± SEM.", "All data were expressed as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 GraphPad Software, La Jolla, CA, USA . For association analysis of the rs12252 allele and genotype, Fisher's exact test was used. Independent samples t-tests were used for normally distributed data.", "Independent samples t-tests were used for normally distributed data. Differences among groups were determined by one-way analysis of variance ANOVA with repeated measures, followed by Bonferroni's post hoc test. P < 0.05 was considered statistically significant.", "P < 0.05 was considered statistically significant. The iFiTM3 snP rs12252 c allele and cc genotype associated with severe hFrs Disease and a higher Plasma hTnV load To determine the clinical significance of IFITM3 SNP in HTNV infection, the relationship between rs12252 SNP and the severity of HFRS in 69 patients were examined. We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients.", "We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients. Then, we stratified these patients into mild and severe, based on the clinical assessment as described in Section \"Material and Methods. \" We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 .", "We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 . The frequency of rs12252 C in severe patients was also higher than those mild patients 68.29 vs. 46.43%, P = 0.013, Figures 1A,B; Table 2 . These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 .", "These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 . For genotypes, 43.90% of the severe patients carried the CC genotype, a significantly higher frequency than the control Han Chinese per 1,000 genomes sequence database 26.92% CC genotype, P = 0.03 as well as mildly infected patients 14.29%, P = 0.02, Figures 1A,B ; Table 2 . However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity.", "However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity. c The plasma HTNV load in CC genotype patients and CT/TT genotype patients, tested by qRCR analysis. Each symbol represents one individual patient.", "Each symbol represents one individual patient. Independent samples t-test was used to test the difference of HTNV load between groups. *P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population.", "*P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population. In addition, we also found that patients with CC genotype had higher plasma viral load in acute phase Figure 1C . These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS.", "These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS. Previous studies reveal that the truncated IFITM3 protein produced by SNP rs12252 C allele Figure 2A , the missing part stands for the truncated 21 amino acids from N-terminal of IFITM3, the intramembrane helix, and transmembrane helix was presented as boxes leads to an impaired anti-influenza activity . .", ". To test the functional significance of this polymorphism in HTNV infection, we transfected the majority T or minority C variant IFITM3 alleles that produce full-length or N-terminally truncated NΔ21 proteins Figure 2A with c-myc-tag to HUVEC and A549 cell using lentivirus vectors Figure 2B . Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material .", "Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material . Indeed, compared with the mock empty vector -infected control, the NΔ21 protein almost lost the ability to inhibit HTNV infection in both HUVEC and A549 cells Figures 2C,D ; Figure S3 in Supplementary Material . To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material .", "To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material . While we observed only a moderate upregulation of IFITM1, 2, and 3 mRNA and protein in HUVECs after more than 24 h postinfection; IFITM1, 2, and 3 mRNA, however, were only transiently upregulated in A549 cells and caused no significant change in protein level. We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually.", "We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually. The effect of siRNAs on the expression of target IFITMs was tested by qPCR in HUVECs Figure S2 in Supplementary Material , and the effect of the best oligo against each IFITMs IFITM1C, IFITM2A, IFITM3B was tested by Western blot in A549 Figure 4A and HUVEC cells Figure 4B . To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h .", "To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h . The cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were significantly suppressed in both HUVEC and A549 cells in response to IFN-α2a treatment. Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells.", "Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells. Knockdown of IFITM1 also partially restored the HTNV level in A549 cells Figures 4C,D . These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection.", "These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection. c-myc-tagged IFITM1, 2, and 3 were expressed in both HUVEC and A549 cells Figure 5A , and the cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were suppressed by IFITM3 overexpression in HUVEC cells Figures 5B-D . They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D .", "They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D . The inhibitory effect of IFITM3 was further confirmed by immunofluorescence analysis of HTNV NP Figure S3 in Supplementary Material . These results were in accordance with the above-described RNAi results.", "These results were in accordance with the above-described RNAi results. To determine whether IFITM3 inhibited HTNV binding or entry, HUVEC and A549 cells were incubated with HTNV moi = 1 at 4°C for 1 h, unbound virus was washed away, and HTNV RNA collected at this time point represents HTNV bound to the cell surface. After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus.", "After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus. As a positive control for inhibition of virus entry, we incubated a parallel group of cells with HTNV at pH = 5.5 as described in Section \"Materials and Methods.\" Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B .", "Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B . iFiTM3 Was Partially localized to laMP1 + late endosomes in the host cells To elucidate the mechanism of IFITM3 function, we investigated the subcellular localization of IFTIM3 in the host cells. IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C .", "IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C . The co-localization of IFITM3 and LAMP1 + late endosomes had also been found in A549 cells . . Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry .", "Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry . , this result provides an evidence for the anti-HTNV mechanism of IFITM3. LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies.", "LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies. We tested the change of previously reported regulators of IFITMs, such as NRAV, NRIR, and miR-130a after HTNV infection, among which NRIR was the only changed one downregulated after HTNV infection Figure 7A ; Figure S4 in Supplementary Material in HUVEC. However, the expression of NRIR was unchanged in A549 cells.", "However, the expression of NRIR was unchanged in A549 cells. We overexpressed NRIR in HUVEC and A549 cells using the pcDNA3.1 vector Figure 7B . Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E .", "Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E . These data suggest that lncRNA NRIR is a negative regulator of IFITM3 transcription. Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae.", "Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae. It causes HFRS, which is an important threat to public health worldwide. It is also a potential weapon for biological terrorism. Reservoir animals, usually rodents, are asymptomatic during persistent infection.", "Reservoir animals, usually rodents, are asymptomatic during persistent infection. Unlike in rodents, Hantavirus infection leads to HFRS and Hantavirus pulmonary syndrome HPS in humans . . The major clinical characteristics of HFRS include fever, hemorrhage, hypotension, and renal injury . , causing severe manifestations and death in some cases.", ", causing severe manifestations and death in some cases. The current standard of care for HFRS relies on symptomatic and supportive treatment. It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS ..", "It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS .. However, no direct antiviral medications are currently available for this illness. Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases.", "Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases. It has been reported that HTNV infection induces a late type I interferon response . . However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified.", "However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified. Therefore, identification of ISGs that are effective against HTNV is an attractive strategy to identify novel therapeutic targets. In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese.", "In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese. The rs12252 C allele and CC genotype are also found to be associated with higher plasma viral load in the early stage of HFRS. We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity.", "We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity. Interestingly, IFITM3 is found to restrict HTNV infection with a mechanism of cellular entry inhibition. Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry.", "Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry. In addition, we find that HTNV infection downregulated lncRNA NRIR 48 h post infection, which negatively regulates the transcription of IFITM3. Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS.", "Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. The antiviral properties of IFITM proteins were identified in 2009 in an RNAi screen for host factors that influence influenza virus replication . .", ". IFITM1, 2, and 3 have been demonstrated to possess antiviral activity in several studies. Everitt et al. demonstrated that the severity of influenza virus infection was greatly increased in IFITM3-knockout mice compared with wild-type animals . .", ". Different IFITM members have also been confirmed to inhibit the cellular entry of multiple virus families including filoviruses, rhabdoviruses, and flaviviruses 7, . . . 30 . For example, HIV-1 and HCV infection are inhibited by IFITM1 . . . . . It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry .", "It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry . . Recent studies suggested that the cellular location of different IFITMs may influence the range of viruses restricted by each protein . . IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles .", "IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles . , whereas IFITM3 confines influenza virus in acidified endosomal compartments . .", ", whereas IFITM3 confines influenza virus in acidified endosomal compartments . . Notably, retrovirus subvirus particles ISVPs , which do not require endosomal acidification for entry, are not inhibited by IFITM3 expression, suggesting that IFITM3 may function at the stage of endosomal entry . .", ". Studies utilizing cell-cell fusion assays have suggested that IFITM3 blocks the entry of enveloped virus by preventing the fusion of the viral membrane with a limiting membrane of the host cell, either the plasma membrane and/or the endosomal membranes. The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes .", "The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes . . In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells.", "In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells. Overexpression of IFITM3 inhibited HTNV infection to HUVEC and A549 cells. IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells.", "IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells. All these results suggest that IFITM3 is an important control factor under natural infection of HTNV. Our results also demonstrate that the effectiveness of IFITM3 is cell type-independent, which is in accordance with the results from similar viruses, such as RVFV . .", ". Binding and entry assays, conducted by controlling the temperature and pH, showed that IFITM3 did not significantly influence HTNV binding but inhibited HTNV entry into HUVEC and A549 cells. Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry.", "Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry. However, we failed in tracking the transportation of HTNV in infected cells possibly due to the lack of fluorescence-labeled virus. In addition, IFITM1 also suppressed HTNV infection in A549 cells.", "In addition, IFITM1 also suppressed HTNV infection in A549 cells. The mechanism underlying anti-HTNV effect of IFITM1 remains undetermined and deserves to be further explored. According to a recent study on the three-dimensional structure of IFITM3, there is a C-terminal transmembrane α-helix and a two-N-terminal intramembrane α-helices shown in Figure 2A as black boxes in IFITM3 . .", ". There are two splice variants that differ by the presence or absence of the first N-terminal 21 amino acids deleted part, shown in Figure 2A as red dotted line . Several SNPs including 13 non-synonymous, 13 synonymous, 1 in-frame stop, and 1 splice site acceptoraltering have been reported in the translated IFITM3 sequence . .", ". Among them, the rare SNP rs12252C allele of IFITM3 truncates the protein as described above, leading to a reduced inhibition of influenza virus infection in A549 cells . . We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro.", "We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro. In Northern European patients hospitalized with seasonal influenza or pandemic influenza A virus, increased homozygosity of the minor C allele of SNP rs12252 in IFITM3 was observed . .", ". In Chinese patients infected with influenza A H1N1 virus, there was also an increased frequency of the C allele and CC genotype of SNP rs12252 . . In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients.", "In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients. Patients carrying the CC genotype also had higher plasma viral loads compared with those with the CT/TT genotype. Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo.", "Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo. We speculate that the much higher level of CC allele at healthy population of Han Chinese compared with Caucasians may place the Chinese at a higher risk for developing severe illness upon HTNV infection, which needs further investigation. LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity.", "LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity. For example, lncRNA NEAT1 is reported to be upregulated by influenza virus or PolyI:C stimulation, which promotes IL-8 expression . .", ". lncRNA NRAV has been shown to negatively regulate the initial transcription of IFITM3 and Mx1 by affecting the histone modification of these genes . . lncRNA NRIR is a non-coding ISG, which has been reported to negatively regulate IFITM1 and Mx1 expression in HCV infection . . Mir-130a was also reported as a regulator of IFITM1 . .", ". Mir-130a was also reported as a regulator of IFITM1 . . In this analysis, lncRNA NRIR was downregulated in HUVECs after HTNV infection for 48 h, overexpression of NRIR negatively regulates the initial transcription of IFITM3, evidenced by the decreased pre-mRNA as well as mRNA levels. NRIR overexpression also facilitated HTNV infection.", "NRIR overexpression also facilitated HTNV infection. These results indicate that the downregulation of NRIR after HTNV infection is possibly involved in the activation of innate immune responses against HTNV infection. We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study.", "We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study. Another lncRNA that can regulate IFITM3, i.e., NRAV NR_038854 , remained unchanged after HTNV infection Figures S4A,B in Supplementary Material . Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material .", "Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material . In conclusion, this study revealed a critical role for IFITM3 in HTNV infection. We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells.", "We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells. In addition, we discovered that the IFITM3 SNP rs12252 C allele and CC genotype correlates with the plasma HTNV load and the severity of HFRS; and the rs12252 C allele produces a truncated IFITM3 protein NΔ21 that attenuates its anti-HTNV function. These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease.", "These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease. aUThOr cOnTribUTiOns ZX-y, BP-y, YC-t, and MH-w performed the experiments; WP-z, BX-f, LY-f, ZY, and JZ-s designed the research; HC-x, YW, and WX analyzed the data; TK and ZC-m provided clinical data; ZX-y and BP-y wrote the paper." ]

[ "Hantaan virus HTNV causes hemorrhagic fever with renal syndrome HFRS . Previous studies have identified interferon-induced transmembrane proteins IFITMs as an interferon-stimulated gene family. However, the role of IFITMs in HTNV infection is unclear.", "However, the role of IFITMs in HTNV infection is unclear. In this study, we observed that IFITM3 single nucleotide polymorphisms SNP rs12252 C allele and CC genotype associated with the disease severity and HTNV load in the plasma of HFRS patients. In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity.", "In vitro experiments showed that the truncated protein produced by the rs12252 C allele exhibited an impaired anti-HTNV activity. We also proved that IFITM3 was able to inhibit HTNV infection in both HUVEC and A549 cells by overexpression and RNAi assays, likely via a mechanism of inhibiting virus entry demonstrated by binding and entry assay. Localization of IFITM3 in late endosomes was also observed.", "Localization of IFITM3 in late endosomes was also observed. In addition, we demonstrated that the transcription of IFITM3 is negatively regulated by an lncRNA negative regulator of interferon response NRIR . Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS.", "Taken together, we conclude that IFITM3, negatively regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the plasma HTNV load and the disease severity of patients with HFRS. Text: associates with the severity of disease, indicating the importance of viremia in the pathogenesis of HFRS . .", ". Therefore, further studies of host factors limiting HTNV infection and influencing antiviral response as well as disease progression are clinically significant and timely. The human family of interferon-induced transmembrane proteins IFITMs was discovered 25 years ago to consist of interferon-stimulated genes ISGs . .", ". This family includes five members, namely, IFITM1, 2, 3, 5, and 10, among which IFITM1, 2, and 3 possess antiviral activity . . Different IFITM proteins have different antiviral spectrum . . For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice .", "For example, IFITM3 has been shown to prevent influenza virus infection in vitro and in mice . , and it also inhibits multiple viruses, including filoviruses, rhabdoviruses, flaviviruses, and even Ebola and Zika virus . . . . . . The antiviral mechanism of IFITM3 is thought to be the restriction of viral entry into cells . .", ". Single nucleotide polymorphisms SNPs are single nucleotide variations in a genetic sequence that occur at an appreciable frequency in the population. Several SNPs has been identified in IFITM3, among which the rs12252 site with C allele results in a N-terminal truncation of IFITM3 protein, leading to impaired inhibition of influenza virus in vitro . .", ". Notably, the frequencies of rs12252 C allele and CC genotype correlate with disease severity in patients infected with influenza virus . . HTNV has been shown to induce a type I interferon response though in later time postinfection . . While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection .", "While overexpression of IFITM1, 2, and 3 in Vero E6 cells has been reported to inhibit HTNV infection . , however, the effect of IFITMs on HTNV infection in human cell lines and its role in HFRS still remain unknown. LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators.", "LncRNA comprises a group of non-coding RNAs longer than 200 nt that function as gene regulators. Some lncRNAs have been shown to play a role in innate immunity . . Among them, negative regulator of interferon response NRIR lncRNA NRIR, also known as lncRNA-CMPK2 is a non-coding ISG that negatively regulates IFITM1 and Mx1 expression in HCV infection . .", ". Notably, IFITM3 is largely homologous to IFITM1, but the role of NRIR in the regulation of IFITM3 in HTNV infection remains unclear. In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans.", "In the present study, we investigate the effect of IFTTM3 on the replication of HTNV and its role in the development of HFRS in humans. We provide primary evidence suggesting that IFITM3, regulated by NRIR, can inhibit HTNV infection and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection.", "This study expands our understanding of the antiviral activity of IFITM3 and enriches our knowledge of innate immune responses to HTNV infection. This study was conducted in accordance with the recommendations of the biomedical research guidelines involving human participants established by the National Health and Family Planning Commission of China. The Institutional Ethics Committee of Tangdu Hospital approved this study.", "The Institutional Ethics Committee of Tangdu Hospital approved this study. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained.", "Before inclusion, all participants were informed of the study objectives and signed the consent form before blood samples and medical records were obtained. Sixty-nine HFRS patients admitted into the Department of Infectious Diseases, Tangdu Hospital between October 2014 and March 2016 were enrolled in this study. All patients were Han Chinese.", "All patients were Han Chinese. The diagnosis of HFRS was made based on typical symptoms and signs as well as positive IgM and IgG antibodies against HTNV in the serum assessed by enzyme linked immunosorbent assay ELISA in our department. The classification of HFRS severity and the exclusion criteria were described as follows .", "The classification of HFRS severity and the exclusion criteria were described as follows . : white blood cells WBC , platelets PLT , blood urea nitrogen BUN , serum creatinine Scr , and heteromorphic lymphocytes that were tested by the Department of Clinical Laboratory shown in Table 1 . According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described .", "According to clinical symptoms and signs, such as fever, effusion, hemorrhage, edema, and renal function, the severity of HFRS can be classified as previously described . : . mild patients were identified with mild renal failure without an obvious oliguric stage; .", "mild patients were identified with mild renal failure without an obvious oliguric stage; . moderate patients were those with obvious symptoms of uremia, effusion bulbar conjunctiva , hemorrhage skin and mucous membrane , and renal failure with a typical oliguric stage; . severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and .", "severe patients had severe uremia, effusion bulbar conjunctiva and either peritoneum or pleura , hemorrhage skin and mucous membrane , and renal failure with oliguria urine output, 50-500 ml/day for ≤5 days or anuria urine output, <50 ml/day for ≤2 days; and . critical patients exhibited ≥1 of the following signs during the illness: refractory shock, visceral hemorrhage, heart failure, pulmonary edema, brain edema, severe secondary infection, and severe renal failure with oliguria urine output, 50-500 ml/day for >5 days, anuria urine output, <50 ml/day for >2 days, or a BUN level of >42.84 mmol/l. Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group.", "Due to the sample quantity required for SNP typing, the mild and moderate patients were assessed together in the mild group, and we combined severe and critical patients as severe group. The exclusion criteria for this study were patients with: . any other kidney disease, . diabetes mellitus, . autoimmune disease, . hematological disease, .", "diabetes mellitus, . autoimmune disease, . hematological disease, . cardiovascular disease, . viral hepatitis types A, B, C, D, or E , or . any other liver disease. In addition, no patients received corticosteroids or other immunomodulatory drugs during the study period . .", ". Genomic DNA was extracted from the peripheral blood of patients using the PureGene DNA Isolation kit Gentra Systems, Minneapolis, MN, USA . The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ .", "The region encompassing the human IFITM3 rs12252 were amplified by PCR forward primer, 5′-GGAAACTGTTGAGAAACCGAA-3′ and reverse primer, 5′-CATACGCACCTTCACGGAGT-3′ . The PCR products were purified and sequenced using an Applied Biosystems 3730xl DNA Analyzer Thermo Scientific, Waltham, MA, USA . The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods .", "The allele frequencies and genotypes of healthy Han Chinese and other groups were obtained from the 1,000 genomes project The HTNV load in plasma samples collected during the acute phase from 24 age-and sex-matched HFRS patients with different genotypes were measured using previously reported methods . . Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA .", "Briefly, viral RNA was extracted from the plasma of HFRS patients using Purelink Viral RNA/DNA Kits Invitrogen, Carlsbad, CA, USA . The SuperScript III Platinum One-Step Quantitative RT-PCR System kit Invitrogen, Carlsbad, CA, USA was employed for the real-time RT-PCR assay. The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′.", "The primers and probe provided by Sangon Biotech, Shanghai, China were as follows: forward, 5′-TACAGAGGGAAATCAATGCC-3′, reverse, 5′-TGTTCAACTCATCTGGATCCTT-3′, and probe, 5′- FAM ATCCCTCACCTTCTGCCTGGCTATC TAMRA -3′. The synthetic S segment of the HTNV standard strain 76-118 RNA transcript was used as the quantitative calibrator. The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators.", "The external standard was the culture supernatant of Vero E6 cells infected with HTNV 76-118, which was quantified using synthetic quantitative calibrators. For each experiment, one aliquot of calibrated 76-118 standard was extracted in parallel with the clinical samples and serially 10-fold diluted with concentrations ranging from 10.56 to 2.56 log10 copies/ml. PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C.", "PCR was performed using an iQ5 Cycler Bio-Rad, Hercules, CA, USA with following conditions: 42°C for 15 min, 95°C for 2 min, and 50 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C. Fluorescence was read during the 72°C step of the final segment of every cycling program. HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator.", "HUVEC cells ScienCell Research Laboratories, Carlsbad, CA, USA were grown in ECM BulletKit ScienCell Research Laboratories, Carlsbad, CA, USA in a 5% CO2 incubator. A549 cells ATCC Cat# CRM-CCL-185, RRID:CVCL_0023 were grown in our laboratory in DMEM with 10% FBS Thermo Scientific, Waltham, MA, USA in a 5% CO2 incubator. Cells were used within passage 10 after primary culture.", "Cells were used within passage 10 after primary culture. HTNV strain 76-118 was cultured in Vero E6 cells ATCC Cat# CRL-1586, RRID:CVCL_0574 in our laboratory and titrated using an immunofluorescence staining assay for HTNV nucleocapsid protein NP as previously described . . The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method.", ". The TCID50 was 10 5 /ml, which was calculated using the Reed-Muench method. The recombinant human IFN-α2a was obtained from PBL Interferon Source Piscataway, NJ, USA and dissolved in the buffer provided by the manufacturer composition not disclosed . HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins.", "HUVEC and A549 cells were infected by incubation with HTNV as indicated moi at 37°C for 60 mins. Subsequently, the virus solution was removed and fresh medium added to the cell culture. Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10.", "Cells were transfected with lentiviral vectors of c-myc-tagged IFITM1, IFITM2, IFITM3, and IFITM3 NΔ21 purchased from GENECHEM, Shanghai, China at a moi of 10. Puromycin 2 μg/ ml for HUVEC and 6 μg/ml for A549 cells was used to create cell lines stably expressing IFITMs. Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA .", "Cells were transfected with control scrambled short interfering RNA siRNA , IFITM1 siRNA, IFITM2 siRNA, or IFITM3 siRNA 10 nM using Lipofectamine 3000 transfection reagent Invitrogen, Carlsbad, CA, USA . SiRNAs were purchased from Origene Rockville, MD, USA , and the sequences were not disclosed. Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA .", "Total RNA was extracted using TRIzol reagent Invitrogen, Carlsbad, CA, USA , and cDNA was synthesized using the K1622 kit Thermo Scientific, Waltham, MA, USA . Quantitative realtime PCR qPCR was performed using SYBR Premix Ex Taq II Takara Biotechnology Co., Dalian, China with a Bio-Rad iQ5 cycler Bio-Rad, Hercules, CA, USA . β-actin was used as the reference gene.", "β-actin was used as the reference gene. The primers Sangon Biotech, Shanghai, China were as follows: IFITM1 forward, 5′-ACTCCGTGAAGTCTAGGGACA-3′ and reverse, 5′-TGTCACAGAGCCGAATACCAG-3′ ; IFITM2 forward, 5′-ATCCCGGTAACCCGATCAC-3′ and reverse, 5′-CTTCCTGTCCCTAGACTTCAC-3′ ; IFITM3 forward, 5′-GGTCTTCGCTGGACACCAT-3′ and reverse, 5′-TGTCCCTAGACTTCACGGAGTA-3′ ; IFITM3 pre-mRNA forward, 5′-CATAGCACGCGGCTCT CAG-3′ and reverse, 5′-CGTCGCCAACCATCTTCCTG-3′ ; HTNV S segment forward, 5′-GCCTGGAGACCATCTGA AAG-3′ and reverse, 5′-AGTATCGGGACGACAAAGGA-3′ ; β-actin forward, 5′-GCTACGTCGCCCTGGACTTC-3′ and reverse, 5′-GTCATAGTCCGCCTAGAAGC-3′ ; NRIR forward, 5′-ATGGTTTTCTGGTGCCTTG-3′ and reverse, 5′-GGAGGTTAGAGGTGTCTGCTG-3′ ; NRAV forward, 5′-TCACTACTGCCCCAGGATCA-3′ and reverse, 5′-GGTGGTCACAGGACTCATGG-3′ . For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA .", "For detection of miR-130a, cDNA was synthesized using the TaqMan microRNA reverse transcription kit Invitrogen, Carlsbad, CA, USA with a specific primer in gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . MiR-130a level was determined using the gene-specific TaqMan assay kit 000454, Invitrogen, Carlsbad, CA, USA . U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control .", "U6 001973, Invitrogen, Carlsbad, CA, USA was used as an endogenous control . . Because the pre-mRNA levels can represent the initial transcription rate . , the primers used to detect the pre-mRNA of IFITM3 were designed targeting the intron of IFITM3 as previously described . . IFITM3 has two exons and one intron.", ". IFITM3 has two exons and one intron. For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon.", "For qPCR of IFITM3 pre-mRNA, the forward primers were positioned in the intron, and the reverse primer was positioned at the beginning of the second exon. For qPCR of IFITM3 mRNA, the forward primers were positioned in the first exon, and the reverse primer was positioned at the beginning of the second exon . .", ". Because the basal expression of IFITM3 is low in A549 cells, we detected IFITM3 mRNA and pre-mRNA in A549 cells following IFN-α2a treatment 20 IU/ml for 12 h after the overexpression of NRIR. Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA .", "Cell lysates were prepared using Radio Immunoprecipitation Assay RIPA buffer Sigma-Aldrich, St. Louis, MO, USA . Equal amounts of protein 20 μg protein/lane were electrophoresed on a 10%-SDS-polyacrylamide gel and electrophoretically transferred to a polyvinylidene difluoride membrane Millipore, Billerica, MA, USA . After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C.", "After blocking with 5% bovine serum albumin in Trisbuffered saline at room temperature for 1 h, the membranes were incubated with antibodies against IFITM1 Proteintech Group Cat# 60074-1-Ig Lot# RRID:AB_2233405 , IFITM2, IFITM3 Proteintech Group Cat# 66081-1-Ig Lot# RRID:AB_11182821 , and β-actin Proteintech, Wuhan, Hubei, China or HTNV NP provided by the Department of Microbiology, The Fourth Military Medical University overnight at 4°C. The membranes were then washed and incubated with HRP-conjugated IgG antibody Cell Signaling Technology, Danvers, MA, USA for 1 h at room temperature. The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film.", "The blots were developed using an enhanced chemiluminescence detection kit Millipore, Billerica, MA, USA and visualized using X-ray film. The blot densities were analyzed using the Quantity One software Bio-Rad, Hercules, CA, USA . In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS.", "In addition, the RIPA buffer contains 50mM Tris pH = 7.4 , 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS. Protease inhibitor cocktail Roche, Basel, Switzerland was added before use. The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 .", "The cells were cultured on glass coverslips Millipore, Billerica, MA, USA until they were semi-confluence and then incubated with HTNV for 60 min moi = 1 . At the indicated times post-HTNV infection, the cells were fixed with 4% PFA, incubated with 0.3% Triton X-100 Sigma-Aldrich, St. Louis, MO, USA , and blocked with 5% BSA for 1 h. Following incubation with a mouse monoclonal antibody against c-myc-tag Sigma-Aldrich, St. Louis, MO, USA, Sigma-Aldrich Cat# M5546 , IFITM3, lysosome-associated membrane glycoprotein 1 LAMP1, Cell Signaling Technology, Danvers, MA, USA , or HTNV NP at 37°C for 2 h, the cells were washed and incubated with anti-rabbit Ig conjugated to Alexa 555 and anti-mouse Ig conjugated to Alexa 488 Abcam, Cambridge, MA, USA secondary antibodies at room temperature for 1 h. The nuclei were counterstained with DAPI. An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images.", "An Olympus BX51 fluorescence microscope system and FV1000 confocal microscopy system Olympus, Tokyo, Japan were used to capture the images. hTnV binding and entry assay Cells transduced with IFITM3 or the empty vector were detached and washed extensively with cold PBS. The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation.", "The cells and HTNV were pre-chilled on ice for 30 min, mixed at a moi of 1 and incubated at 4°C for 1 h with rotation. Part of cells were washed extensively with ice-cold PBS and harvested for binding assay. Another part of cells were switched to 37°C for 2 h to allow HTNV entry.", "Another part of cells were switched to 37°C for 2 h to allow HTNV entry. The HTNV that remained on the cell surface was removed by treatment with proteinase K 0.1 mg/ml, Thermo Scientific, Waltham, MA, USA . To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl.", "To achieve direct entry of HTNV into cells by virus-plasma membrane fusion as a positive control, cells were pre-chilled on ice for 10 min with 20 mM NH4Cl. Adsorption of HTNV moi = 1 was performed at 4°C for 1 h. The cells were then washed, and fusion of the virus with the plasma membrane was triggered by incubation in low pH medium 20 mM sodium succinate, pH = 5.5 for 10 min at 37°C. Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl .", "Infection was followed by incubation for 2 h at 37°C in the presence of 20 mM NH4Cl . . qPCR analysis of the HTNV S segment was conducted to evaluate the influence of IFITMs on HTNV cell adhesion and HTNV entry. All data were expressed as the mean ± SEM.", "All data were expressed as the mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 GraphPad Software, La Jolla, CA, USA . For association analysis of the rs12252 allele and genotype, Fisher's exact test was used. Independent samples t-tests were used for normally distributed data.", "Independent samples t-tests were used for normally distributed data. Differences among groups were determined by one-way analysis of variance ANOVA with repeated measures, followed by Bonferroni's post hoc test. P < 0.05 was considered statistically significant.", "P < 0.05 was considered statistically significant. The iFiTM3 snP rs12252 c allele and cc genotype associated with severe hFrs Disease and a higher Plasma hTnV load To determine the clinical significance of IFITM3 SNP in HTNV infection, the relationship between rs12252 SNP and the severity of HFRS in 69 patients were examined. We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients.", "We sequenced 300 bp of the IFITM3 locus encompassing SNP rs12252 in all enrolled patients. Then, we stratified these patients into mild and severe, based on the clinical assessment as described in Section \"Material and Methods. \" We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 .", "We found a significantly higher frequency of the C allele among severe HFRS patients compared with the healthy Han Chinese in the 1,000 genomes sequence database 68.29 vs. 52.16%, P = 0.0076 . The frequency of rs12252 C in severe patients was also higher than those mild patients 68.29 vs. 46.43%, P = 0.013, Figures 1A,B; Table 2 . These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 .", "These data suggest that harboring rs12252 C allele increases the risk of suffering severe disease in HTNV-infected individuals, with an odds ratio 95% CI of 2.124 1.067-4.230 . For genotypes, 43.90% of the severe patients carried the CC genotype, a significantly higher frequency than the control Han Chinese per 1,000 genomes sequence database 26.92% CC genotype, P = 0.03 as well as mildly infected patients 14.29%, P = 0.02, Figures 1A,B ; Table 2 . However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity.", "However, mildly ill individuals did not exhibit a Fisher's exact test was used to test the association between rs12252 allele/genotype and HFRS severity. c The plasma HTNV load in CC genotype patients and CT/TT genotype patients, tested by qRCR analysis. Each symbol represents one individual patient.", "Each symbol represents one individual patient. Independent samples t-test was used to test the difference of HTNV load between groups. *P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population.", "*P < 0.05, **P < 0.01. significantly different genotype frequencies compared with the Han Chinese population. In addition, we also found that patients with CC genotype had higher plasma viral load in acute phase Figure 1C . These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS.", "These results support the notion that the normal function of IFITM3 plays a critical role in the immune response to HTNV infection in vivo, which has a substantial influence on the clinical manifestation of HFRS. Previous studies reveal that the truncated IFITM3 protein produced by SNP rs12252 C allele Figure 2A , the missing part stands for the truncated 21 amino acids from N-terminal of IFITM3, the intramembrane helix, and transmembrane helix was presented as boxes leads to an impaired anti-influenza activity . .", ". To test the functional significance of this polymorphism in HTNV infection, we transfected the majority T or minority C variant IFITM3 alleles that produce full-length or N-terminally truncated NΔ21 proteins Figure 2A with c-myc-tag to HUVEC and A549 cell using lentivirus vectors Figure 2B . Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material .", "Then, we challenged the cells with HTNV at moi = 1 for 24 h and found that cells with the minority C variant were more susceptible to HTNV infection with higher expression of HTNV S segment Figure 2C and more positive of HTNV NP Figure S3 in Supplementary Material . Indeed, compared with the mock empty vector -infected control, the NΔ21 protein almost lost the ability to inhibit HTNV infection in both HUVEC and A549 cells Figures 2C,D ; Figure S3 in Supplementary Material . To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material .", "To determine the role of HTNV infection in inducing IFITMs, qPCR as well as Western blot of IFITMs were conducted in HUVEC and A549 cells Figures 3A,B ; Figure S1 in Supplementary Material . While we observed only a moderate upregulation of IFITM1, 2, and 3 mRNA and protein in HUVECs after more than 24 h postinfection; IFITM1, 2, and 3 mRNA, however, were only transiently upregulated in A549 cells and caused no significant change in protein level. We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually.", "We knocked down the IFITM1, 2, and 3 expression by transfection of their siRNAs individually. The effect of siRNAs on the expression of target IFITMs was tested by qPCR in HUVECs Figure S2 in Supplementary Material , and the effect of the best oligo against each IFITMs IFITM1C, IFITM2A, IFITM3B was tested by Western blot in A549 Figure 4A and HUVEC cells Figure 4B . To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h .", "To assess the role of IFITMs in anti-HTNV effect of IFN-α2a, IFITM1, 2, and 3 were knocked down respectively by transfecting the above-tested oligoes for 12 h, followed by IFN-α2a treatment 20 IU/ml for another 12 h . The cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were significantly suppressed in both HUVEC and A549 cells in response to IFN-α2a treatment. Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells.", "Notably, knockdown of IFITM3 significantly restored the levels of HTNV S segment and NP in HUVEC and A549 cells. Knockdown of IFITM1 also partially restored the HTNV level in A549 cells Figures 4C,D . These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection.", "These results demonstrate that To assess the anti-HTNV effects of IFITMs, we tested the effect of overexpressed IFITM1, 2, and 3 on HTNV infection. c-myc-tagged IFITM1, 2, and 3 were expressed in both HUVEC and A549 cells Figure 5A , and the cells were then challenged with HTNV moi = 1 for 24 h. The HTNV S segment and NP levels were suppressed by IFITM3 overexpression in HUVEC cells Figures 5B-D . They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D .", "They were also suppressed by expressing IFITM1 and IFITM3 in A549 cells Figures 5B-D . The inhibitory effect of IFITM3 was further confirmed by immunofluorescence analysis of HTNV NP Figure S3 in Supplementary Material . These results were in accordance with the above-described RNAi results.", "These results were in accordance with the above-described RNAi results. To determine whether IFITM3 inhibited HTNV binding or entry, HUVEC and A549 cells were incubated with HTNV moi = 1 at 4°C for 1 h, unbound virus was washed away, and HTNV RNA collected at this time point represents HTNV bound to the cell surface. After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus.", "After virus binding, the cells were shifted to 37°C for 2 h to allow HTNV internalization, and HTNV RNA collected at this time point represents cell-internalized virus. As a positive control for inhibition of virus entry, we incubated a parallel group of cells with HTNV at pH = 5.5 as described in Section \"Materials and Methods.\" Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B .", "Expression of IFITM3 did not affect HTNV binding Figure 6A but significantly suppressed HTNV entry in both HUVEC and A549 cells Figure 6B . iFiTM3 Was Partially localized to laMP1 + late endosomes in the host cells To elucidate the mechanism of IFITM3 function, we investigated the subcellular localization of IFTIM3 in the host cells. IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C .", "IFITM3 was found partially localized to LAMP1 + late endosomes in HUVECs analyzed by confocal microscopy Figure 6C . The co-localization of IFITM3 and LAMP1 + late endosomes had also been found in A549 cells . . Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry .", "Because the transfer into LAMP1 + late endosomes is a necessary step for HTNV entry . , this result provides an evidence for the anti-HTNV mechanism of IFITM3. LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies.", "LncRNA-and microRNA-mediated regulation of IFITM3 has been reported in several studies. We tested the change of previously reported regulators of IFITMs, such as NRAV, NRIR, and miR-130a after HTNV infection, among which NRIR was the only changed one downregulated after HTNV infection Figure 7A ; Figure S4 in Supplementary Material in HUVEC. However, the expression of NRIR was unchanged in A549 cells.", "However, the expression of NRIR was unchanged in A549 cells. We overexpressed NRIR in HUVEC and A549 cells using the pcDNA3.1 vector Figure 7B . Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E .", "Importantly, overexpression of NRIR significantly suppressed IFITM3 mRNA and pre-mRNA levels and facilitated HTNV infection in HUVEC and A549 cells Figures 7C-E . These data suggest that lncRNA NRIR is a negative regulator of IFITM3 transcription. Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae.", "Hantaan virus is an enveloped, negative-sense RNA virus from the genus Hantavirus within the family Bunyaviridae. It causes HFRS, which is an important threat to public health worldwide. It is also a potential weapon for biological terrorism. Reservoir animals, usually rodents, are asymptomatic during persistent infection.", "Reservoir animals, usually rodents, are asymptomatic during persistent infection. Unlike in rodents, Hantavirus infection leads to HFRS and Hantavirus pulmonary syndrome HPS in humans . . The major clinical characteristics of HFRS include fever, hemorrhage, hypotension, and renal injury . , causing severe manifestations and death in some cases.", ", causing severe manifestations and death in some cases. The current standard of care for HFRS relies on symptomatic and supportive treatment. It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS ..", "It has been confirmed that the plasma viral load is associated with the severity of HFRS, implicating the importance of viremia in the pathogenesis of HFRS .. However, no direct antiviral medications are currently available for this illness. Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases.", "Interferon is the key molecule for the antiviral response and has been used as an antiviral medicine in many diseases. It has been reported that HTNV infection induces a late type I interferon response . . However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified.", "However, the set of ISGs required for IFN-mediated inhibition of HTNV has not yet been identified. Therefore, identification of ISGs that are effective against HTNV is an attractive strategy to identify novel therapeutic targets. In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese.", "In this study, we demonstrated a significantly high frequency of the rs12252 C allele and CC genotype among HFRS patients with severe illness compared with mildly infected individuals and the healthy Han Chinese. The rs12252 C allele and CC genotype are also found to be associated with higher plasma viral load in the early stage of HFRS. We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity.", "We also discovered that HTNV infection induces IFITMs, and the truncated IFITM3 produced by rs12252 C allele exhibits significantly decreased anti-HTNV activity. Interestingly, IFITM3 is found to restrict HTNV infection with a mechanism of cellular entry inhibition. Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry.", "Indeed, IFITM3 is localized to the late endosome in the host cells, which is a necessary structure for HTNV entry. In addition, we find that HTNV infection downregulated lncRNA NRIR 48 h post infection, which negatively regulates the transcription of IFITM3. Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS.", "Collectively, these results suggest that IFITM3, regulated by NRIR, inhibits HTNV infection, and its SNP rs12252 correlates with the disease severity and viral load in patients with HFRS. The antiviral properties of IFITM proteins were identified in 2009 in an RNAi screen for host factors that influence influenza virus replication . .", ". IFITM1, 2, and 3 have been demonstrated to possess antiviral activity in several studies. Everitt et al. demonstrated that the severity of influenza virus infection was greatly increased in IFITM3-knockout mice compared with wild-type animals . .", ". Different IFITM members have also been confirmed to inhibit the cellular entry of multiple virus families including filoviruses, rhabdoviruses, and flaviviruses 7, . . . 30 . For example, HIV-1 and HCV infection are inhibited by IFITM1 . . . . . It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry .", "It is commonly believed that IFITMs restrict viral infection at the stage of cellular entry . . Recent studies suggested that the cellular location of different IFITMs may influence the range of viruses restricted by each protein . . IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles .", "IFITM1 prevents HCV entry because it colocalizes with CD81 on the cell membrane, interrupting the endocytosis of HCV particles . , whereas IFITM3 confines influenza virus in acidified endosomal compartments . .", ", whereas IFITM3 confines influenza virus in acidified endosomal compartments . . Notably, retrovirus subvirus particles ISVPs , which do not require endosomal acidification for entry, are not inhibited by IFITM3 expression, suggesting that IFITM3 may function at the stage of endosomal entry . .", ". Studies utilizing cell-cell fusion assays have suggested that IFITM3 blocks the entry of enveloped virus by preventing the fusion of the viral membrane with a limiting membrane of the host cell, either the plasma membrane and/or the endosomal membranes. The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes .", "The results obtained using two-photon laser scanning and fluorescence lifetime imaging FLIM suggest that IFITM proteins may reduce membrane fluidity and increase the spontaneous positive curvature in the outer leaflet of membranes . . In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells.", "In the present study, we demonstrated that IFN-α2a 20 U/ ml significantly inhibited HTNV infection, siRNA-mediated depletion of IFITM3 alone significantly mitigated the antiviral effect of IFN-α2a in both HUVEC and A549 cells, whereas depletion of IFITM1 alone alleviated the antiviral effect of IFN-α2a in A549 cells. Overexpression of IFITM3 inhibited HTNV infection to HUVEC and A549 cells. IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells.", "IFITM1 overexpression was also effective in inhibition of HTNV in A549 cells. All these results suggest that IFITM3 is an important control factor under natural infection of HTNV. Our results also demonstrate that the effectiveness of IFITM3 is cell type-independent, which is in accordance with the results from similar viruses, such as RVFV . .", ". Binding and entry assays, conducted by controlling the temperature and pH, showed that IFITM3 did not significantly influence HTNV binding but inhibited HTNV entry into HUVEC and A549 cells. Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry.", "Indeed, IFITM3 partially localizes to the late endosome of the host cells, which is a necessary site for the HTNV entry. However, we failed in tracking the transportation of HTNV in infected cells possibly due to the lack of fluorescence-labeled virus. In addition, IFITM1 also suppressed HTNV infection in A549 cells.", "In addition, IFITM1 also suppressed HTNV infection in A549 cells. The mechanism underlying anti-HTNV effect of IFITM1 remains undetermined and deserves to be further explored. According to a recent study on the three-dimensional structure of IFITM3, there is a C-terminal transmembrane α-helix and a two-N-terminal intramembrane α-helices shown in Figure 2A as black boxes in IFITM3 . .", ". There are two splice variants that differ by the presence or absence of the first N-terminal 21 amino acids deleted part, shown in Figure 2A as red dotted line . Several SNPs including 13 non-synonymous, 13 synonymous, 1 in-frame stop, and 1 splice site acceptoraltering have been reported in the translated IFITM3 sequence . .", ". Among them, the rare SNP rs12252C allele of IFITM3 truncates the protein as described above, leading to a reduced inhibition of influenza virus infection in A549 cells . . We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro.", "We demonstrated that truncated IFITM3 protein also loses the ability to inhibit HTNV infection in vitro. In Northern European patients hospitalized with seasonal influenza or pandemic influenza A virus, increased homozygosity of the minor C allele of SNP rs12252 in IFITM3 was observed . .", ". In Chinese patients infected with influenza A H1N1 virus, there was also an increased frequency of the C allele and CC genotype of SNP rs12252 . . In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients.", "In the present study, we observed an increased frequency of the C allele and CC genotype of SNP rs12252 in severely infected HFRS patients compared with healthy control and mildly affected patients. Patients carrying the CC genotype also had higher plasma viral loads compared with those with the CT/TT genotype. Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo.", "Given the impaired function of the IFITM3 protein produced by the C mutation, and the fact that enrichment of the rs12252 C allele in patients with severe disease and the higher viral load in patients with the CC genotype, this founding suggests that IFITM3 plays a pivotal role in the anti-HTNV response in vivo. We speculate that the much higher level of CC allele at healthy population of Han Chinese compared with Caucasians may place the Chinese at a higher risk for developing severe illness upon HTNV infection, which needs further investigation. LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity.", "LncRNAs are a group of non-coding RNAs longer than 200 nt that function as gene regulators, playing a role in regulating multiple cellular functions, including the innate immunity. For example, lncRNA NEAT1 is reported to be upregulated by influenza virus or PolyI:C stimulation, which promotes IL-8 expression . .", ". lncRNA NRAV has been shown to negatively regulate the initial transcription of IFITM3 and Mx1 by affecting the histone modification of these genes . . lncRNA NRIR is a non-coding ISG, which has been reported to negatively regulate IFITM1 and Mx1 expression in HCV infection . . Mir-130a was also reported as a regulator of IFITM1 . .", ". Mir-130a was also reported as a regulator of IFITM1 . . In this analysis, lncRNA NRIR was downregulated in HUVECs after HTNV infection for 48 h, overexpression of NRIR negatively regulates the initial transcription of IFITM3, evidenced by the decreased pre-mRNA as well as mRNA levels. NRIR overexpression also facilitated HTNV infection.", "NRIR overexpression also facilitated HTNV infection. These results indicate that the downregulation of NRIR after HTNV infection is possibly involved in the activation of innate immune responses against HTNV infection. We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study.", "We have also evaluated other potential regulators of IFITM3 before we choose NRIR for further study. Another lncRNA that can regulate IFITM3, i.e., NRAV NR_038854 , remained unchanged after HTNV infection Figures S4A,B in Supplementary Material . Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material .", "Additionally, miR-130a, which potentially regulate IFITM3, was also unaltered after HTNV infection Figures S4C,D in Supplementary Material . In conclusion, this study revealed a critical role for IFITM3 in HTNV infection. We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells.", "We demonstrated, for the first time to our knowledge, that IFITM3 is a newly identified anti-HTNV ISG; its expression is negatively regulated by NRIR; and its antiviral activity seems via a mechanism of inhibiting virus entry into the host cells. In addition, we discovered that the IFITM3 SNP rs12252 C allele and CC genotype correlates with the plasma HTNV load and the severity of HFRS; and the rs12252 C allele produces a truncated IFITM3 protein NΔ21 that attenuates its anti-HTNV function. These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease.", "These results provide new insights into the role of IFITM3 in regulating innate immunity against HTNV infection, which is the basis for identifying new targets to develop novel agent against this worldwide infectious disease. aUThOr cOnTribUTiOns ZX-y, BP-y, YC-t, and MH-w performed the experiments; WP-z, BX-f, LY-f, ZY, and JZ-s designed the research; HC-x, YW, and WX analyzed the data; TK and ZC-m provided clinical data; ZX-y and BP-y wrote the paper." ]

[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. Lourenco, J. et al. Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic. .1101/2020.03.24.20042291 10. United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2019: Data Booket. ST/ESA/SER.A/424. .. 11.", "World Population Prospects 2019: Data Booket. ST/ESA/SER.A/424. .. 11. Verity, R. et al. Estimates ofthe severity of COVID-19 disease. Lancet Infect Dis in press, .. 12. Walker, P. G. T. et al. Report 12: The Global Impact of COVID-19 and Strategies for Mitigation and Suppression. 13.", "13. Fraser, C. Estimating Individual and Household Reproduction Numbers in an Emerging Epidemic. PL05 ONE 2, e758 .. 14. Cori, A., Ferguson, N. M., Fraser, C. & Cauchemez, S. A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics. Am. J. Epidemiol.", "Am. J. Epidemiol. 178, 1505—1512 20131 15. 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The Guardian after-italian-lockdown .. 22. Bundesministerium. Coronavirus - Aktuelle MaBnahmen. MaBnahmen.html .. 23.", "Bundesministerium. Coronavirus - Aktuelle MaBnahmen. MaBnahmen.html .. 23. Federal Public Service. Coronavirus : Phase 2 maintained, transition to the federal phase and additional measures. transition-to-the-federal-phase-and-additional-measures/ .. 24. Belgium.be. Coronavirus: reinforced measures | Belgium.be. .. 25. Federal Public Service. Protect yourself and protect the others. coronavirus.be/en/2020/03/10/protect-yourself—and-protect-the-others/ .. 26. Wikipedia.", "Protect yourself and protect the others. coronavirus.be/en/2020/03/10/protect-yourself—and-protect-the-others/ .. 26. Wikipedia. 2020 coronavirus pandemic in Denmark. Wikimedia Foundation 27. Stephensen, Emma K|inker; Hansen, T. S. Danmark lukker ned: Her er regeringens nye tiltag. TV2 20201 28. Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. NRK vaere-smittet-1.14958149 .. 44.", "NRK vaere-smittet-1.14958149 .. 44. Norweigen Government. The Government is establishing clear quarantine and isolation rules. regjeringen.no quarantine-and-isolation-rules/id2693647/ .. 45. Wikipedia. 2020 coronavirus pandemic in Spain. Wikimedia Foundation 46. Gabinete de Prensa. El Gobierno anuncia nuevas medidas para evitar la extension del nuevo coronavirus COVID-19. Gobierno de Espana .. 47. Gabinete de Prensa.", "Gobierno de Espana .. 47. Gabinete de Prensa. El Consejo Interterritorial del SNS acuerda medidas concretas para zonas con transmision comunitaria significativa de coronavirus. Gobierno de Espana .. 48. Folkhalsomyndigheten. Larosaten och gymnasieskolor uppmanas nu att bedriva distansundervisning. Folkhdlsomyndigheten press/nyhetsarkiv/2020/mars/larosaten-och-gymnasieskolor-uppmanas-nu-att-bedriva- distansundervisning.. 49. The Local. Sweden bans large events to halt coronavirus spread.", "The Local. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. 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Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. 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Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. 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Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. 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Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. 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Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. 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Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. 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Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. 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Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions NPIs to reduce COVID-19 mortality and healthcare demand Report 9 . disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy.", "The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv .. 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. .. 7. Jombart, T. et al.", ".. 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761..1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. . .1101/2020.03.19.20039107. 9.", ". .1101/2020.03.19.20039107. 9. 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Styrelsen for Patientsikkerhed.", "Politi. Nye tiltag mod covid-19. Politi myndighederne/nye-tiltag-mod-covid-19 .. 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\\aabud til enkeltpersoner coronavirus/covid-19 . retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local.", "Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban.", "The Local to-fight-coronavirus-pandemic .. 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian spain-takes-over-private-healthcare-amid-more-european-lockdowns .. 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten.", "Wikimedia Foundation 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News .. 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186.", "Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. 1730186. 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut AQ_Liste.html .. 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo.", "Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute liano&menu=multimedia&p=video&id=2052 .. 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN .. 40. Attualita.", "CNN .. 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today teatri-locali-chiusi-nuovo-decreto.html .. 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet.", "DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale .. 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions .. 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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[ "Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus SARS-CoV-Z and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.", "In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.", "Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects.", "This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier.", "We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown 11th March , although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number.", "Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value.", "This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March 95% credible interval 21,000-120,000 . Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.", "Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.", "The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly.", "If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/.", "SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe.", "Imperial College London ., doi: 1 Introduction Following the emergence of a novel coronavirus SARS-CoV-Z in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.", "These large-scale non-pharmaceutical interventions vary between countries but include social distancing such as banning large gatherings and advising individuals not to socialize outside their households , border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection.", "The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity.", "If Rt is greater than 1, then infections will increase dependent on how much greater than 1 the reproduction number is until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement.", "Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g.", "Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups e.g. contacts of cases . Looking at case data, therefore, gives a systematically biased view of trends.", "Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed.", "Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt .", "In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds Bayesian credible intervals of the total populations infected attack rates , case detection probabilities, and the reproduction number over time Rt . We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts.", "The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions.", "Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 .", "Italy was the first to begin intervention measures, and other countries followed soon afterwards Figure 1 . Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions.", "Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced Figure 2- 12 . Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period.", "Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries.", "Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6.", "For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected Figure 2 than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 .", "In Italy, our results suggest that, cumulatively, 5.9 1.9-15.2 million people have been infected as of March 28th, giving an attack rate of 9.8% 3.2%-25% of the population Table 1 . Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% 7.0 18-19 million people have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected.", "Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 240,000-1,500,000 people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths.", "Country % of total population infected mean 95% credible intervall Austria 1.1% 0.36%-3.1% Belgium 3.7% 1.3%-9.7% Denmark 1.1% 0.40%-3.1% France 3.0% 1.1%-7.4% Germany 0.72% 0.28%-1.8% Italy 9.8% 3.2%-26% Norway 0.41% 0.09%-1.2% Spain 15% 3.7%-41% Sweden 3.1% 0.85%-8.4% Switzerland 3.2% 1.3%-7.6% United Kingdom 2.7% 1.2%-5.4% 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 3.01-4.66 , which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers Appendix 8.4.2, Appendix 8.4.6 . The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread.", "The initial reproduction numbers are also uncertain due to a importation being the dominant source of new infections early in the epidemic, rather than local transmission b possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g.", "Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths e.g. Italy, Spain , suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values.", "Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 0.14-2.14 for Norway to a posterior mean of2.64 1.40-4.18 for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range.", "In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries .", "Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 posterior probability of being less than 1.0 is 44% on average across the countries . We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates.", "There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic e.g. Germany, UK, Norway . Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death.", "Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well Bayesian goodness of fit tests .", "For all countries, our model fits observed deaths data well Bayesian goodness of fit tests . We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths Appendix 8.3 . The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 .", "The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect Figure 1, Figure 4 . However, when doing a sensitivity analysis Appendix 8.4.3 with uninformative prior distributions where interventions can increase deaths we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt.", "Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval CI , light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention.", "The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot.", "Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred.", "Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model assuming no intervention had taken place . Estimated averted deaths over this time period as a result of the interventions.", "Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e.", "2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented and R, = R0 i.e. the initial reproduction number estimated before interventions . Again, the assumption in these predictions is that intervention impact is the same across countries and time.", "Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed Figure 3 . Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C .", "Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C . By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions.", "We find that, across 11 countries, since the beginning of the epidemic, 59,000 21,000-120,000 deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 13,000- 84,000 and 16,000 5,400-35,000 deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted.", "Even in the UK, which is much earlier in its epidemic, we predict 370 73-1,000 deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher.", "lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for a Italy and b Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6.", "Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% no effect on transmissibility ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 red or was subsequent to other interventions green . Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals.", "Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced.", "No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported.", "We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 .", "Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% l.9%-ll% with considerable variation between countries Table 1 . Our estimates imply that the populations in Europe are not close to herd immunity \"50-75% if R0 is 2-4 . Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly.", "Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available.", "Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths Italy, Spain . lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths.", "lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention.", "Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis .", "Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified see Appendix 8.4 for sensitivity analysis . While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model.", "To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics.", "For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 .", "For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear on the logarithmic scale reduction in deaths see Figure 10 . The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher 38,000 deaths averted than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March.", "Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics.", "Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths.", "While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections.", "We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic.", "Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies.", "For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0.", "Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic.", "We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions e.g. Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g.", "Italy, Spain strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths e.g. Germany, UK . We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6.", "We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data.", "These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time.", "However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, see the sensitivity analysis reported in Appendix 8.4.3 and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates.", "This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country.", "For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC European Centre of Disease Control , where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19.", "The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all.", "We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions.", "For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely .", "We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely . In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards .", "The date of the school closure is taken to be the effective date when the schools started to be closed ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards . Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated.", "These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals e.g. UK or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China.", "These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact.", "Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown.", "Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g.", "We include special cases where these are not explicitly mentioned on government websites but are enforced by the police e.g. France . The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented.", "We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples Norway/Denmark have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19.", "Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 .", "6 Methods Summary A Visual summary of our model is presented in Figure 5 details in Appendix 8.1 and 8.2 . Replication code is available at We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix .", "The modelled deaths are informed by an infection-to-onset distribution time from infection to the onset of symptoms , an onset-to-death distribution time from the onset of symptoms to death , and the population-averaged infection fatality ratio adjusted for the age structure and contact patterns of each country, see Appendix . Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number.", "The modelled number of infections is informed by the serial interval distribution the average time from infection of one person to the time at which they infect another and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components.", "Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible.", "We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths and get stronger as time progresses . To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set.", "To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 .", "We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust Appendix 8.4 . 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel.", "This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E Dam that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days.", "Dam is assumed to follow a negative binomial distribution with The expected number of deaths 1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model.", "To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr probability of death given infection 9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups.", "The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates the unadjusted ifr, referred to here as ifr’ in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing.", "The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates.", "We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period infection to onset of symptoms or infection- to-onset distribution and the time between onset of symptoms and death onset-to-death . The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86.", "The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio.", "The infection-to-death distribution is therefore given by: um ~ ifrm ~ Gamma 5.1,0.86 + Gamma 18.8,0.45 Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution mean 23.9 days . Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left.", "Right, survival probability of infected individuals per day given the infection fatality ratio 1% and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process.", "8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 .", "To model the number ofinfections over time we need to specify a serial interval distribution g with density g T , the time between when a person gets infected and when they subsequently infect another other people , which we choose to be Gamma distributed: g ~ Gamma 6.50.62 . The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days.", "Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg T dT fors = 2,3, and 91 = fT=Og T dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution.", "Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times.", "The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures k=l , self—isolating if ill k=2 , banning of public events k=3 , any government intervention in place k=4 , implementing a partial or complete lockdown k=5 and encouraging social distancing and isolation k=6 . We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise.", "We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” k=4 indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention.", "Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential.", "Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp — 212:1 O Rheum - The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0 1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma . 5,1 .", "5,1 . The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information.", "The prior distribution for R0 was chosen to be R0,m ~ Normal 2.4, IKI with K ~ Normal 0,0.5 , Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 .", "From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential T , where T~Exponential 0.03 . These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model.", "We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo HMC sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples.", "We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed see below . 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation.", "8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data non-cumulative and fit our model. We forecast what the model predicts for these three days.", "We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8.", "The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible.", "Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution .", "The Rhat statistic measures whether our Markov Chain Monte Carlo MCMC chains have converged to the equilibrium distribution the correct posterior distribution . Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged.", "Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt.", "8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale.", "To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK.", "Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic.", "Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days.", "For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings.", "The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0.", "This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping.", "The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval SI distributions means between 5 and 8 days . We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval SI distribution means between 5 and 8 days. We use 6.5 days in our main analysis.", "We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 .", "To avoid collinearity, we ran 6 separate models, with effects summarized below compare with the main analysis in Figure 4 . In this series of univariate analyses, we find Figure 15 that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis.", "This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others.", "The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution.", "8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data.", "We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK .", "Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others such as the UK . To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain.", "Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model.", "Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots.", "See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model.", "We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g r with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = 1 + % .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 red vs R FO black Figure 18: Our estimated R0 black versus theoretically derived Ru red from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future.", "8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions up to 28 March and forecasts after for all countries except Italy and Spain from our model with interventions blue and from the no interventions counterfactual model pink ; credible intervals are shown one week into the future. DOI: Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions.", "Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys.", "Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport.", "restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities primary ordered schools also shut on 16th .26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people.", "Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions.", "People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred.", "Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure.", "All businesses where Social distancing this cannot be realised have been closed in all encouraged states kantons .54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al.", "Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus SARS-CoV2 . Science . .1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11. 3. Worldometers.info. Hong Kong: coronavirus cases. rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4.", "3. Worldometers.info. 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