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      Transmission dynamics of SARS-CoV-2 in a strictly-Orthodox Jewish community in the UK

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          Abstract

          Some social settings such as households and workplaces, have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020, reaching 64.3% seroprevalence within 10 months, and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data, and a network model of people and places represented as a stochastic graph rewriting system, we estimated the relative contribution of transmission in households, schools and religious institutions to the epidemic, and the relative risk of infection in each of these settings. All congregate settings were important for transmission, with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher general-community transmission rate for women (3.3-fold), and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations.

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          Age-dependent effects in the transmission and control of COVID-19 epidemics

          The COVID-19 pandemic has shown a markedly low proportion of cases among children1-4. Age disparities in observed cases could be explained by children having lower susceptibility to infection, lower propensity to show clinical symptoms or both. We evaluate these possibilities by fitting an age-structured mathematical model to epidemic data from China, Italy, Japan, Singapore, Canada and South Korea. We estimate that susceptibility to infection in individuals under 20 years of age is approximately half that of adults aged over 20 years, and that clinical symptoms manifest in 21% (95% credible interval: 12-31%) of infections in 10- to 19-year-olds, rising to 69% (57-82%) of infections in people aged over 70 years. Accordingly, we find that interventions aimed at children might have a relatively small impact on reducing SARS-CoV-2 transmission, particularly if the transmissibility of subclinical infections is low. Our age-specific clinical fraction and susceptibility estimates have implications for the expected global burden of COVID-19, as a result of demographic differences across settings. In countries with younger population structures-such as many low-income countries-the expected per capita incidence of clinical cases would be lower than in countries with older population structures, although it is likely that comorbidities in low-income countries will also influence disease severity. Without effective control measures, regions with relatively older populations could see disproportionally more cases of COVID-19, particularly in the later stages of an unmitigated epidemic.
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            Mobility network models of COVID-19 explain inequities and inform reopening

            The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.
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              Scientific and ethical basis for social-distancing interventions against COVID-19

              On Dec 31, 2019, the WHO China Country Office received notice of a cluster of pneumonia cases of unknown aetiology in the Chinese city of Wuhan, Hubei province. 1 The incidence of coronavirus disease 2019 (COVID-19; caused by severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) has since risen exponentially, now affecting all WHO regions. The number of cases reported to date is likely to represent an underestimation of the true burden as a result of shortcomings in surveillance and diagnostic capacity affecting case ascertainment in both high-resource and low-resource settings. 2 By all scientifically meaningful criteria, the world is undergoing a COVID-19 pandemic. In the absence of any pharmaceutical intervention, the only strategy against COVID-19 is to reduce mixing of susceptible and infectious people through early ascertainment of cases or reduction of contact. In The Lancet Infectious Diseases, Joel Koo and colleagues 3 assessed the potential effect of such social distancing interventions on SARS-CoV-2 spread and COVID-19 burden in Singapore. The context is worthy of study, since Singapore was among the first settings to report imported cases, and has so far succeeded in preventing community spread. During the 2003 severe acute respiratory syndrome coronavirus (SARS-CoV) outbreak in Singapore, numerous non-pharmaceutical interventions were implemented successfully, including effective triage and infection control measures in health-care settings, isolation and quarantine of patients with SARS and their contacts, and mass screening of school-aged children for febrile illness. 4 Each of these measures represented an escalation of typical public health action. However, the scale and disruptive impact of these interventions were small compared with the measures that have been implemented in China in response to COVID-19, including closure of schools, workplaces, roads, and transit systems; cancellation of public gatherings; mandatory quarantine of uninfected people without known exposure to SARS-CoV-2; and large-scale electronic surveillance.5, 6 Although these actions have been praised by WHO, 5 the possibility of imposing similar measures in other countries raises important questions. Populations for whom social-distancing interventions have been implemented require and deserve assurance that the decision to enact these measures is informed by the best attainable evidence. For a novel pathogen such as SARS-CoV-2, mathematical modelling of transmission under differing scenarios is the only viable and timely method to generate such evidence. Koo and colleagues 3 adapted an existing influenza epidemic simulation model 7 using granular data on the composition and behaviour of the population of Singapore to assess the potential consequences of specific social-distancing interventions on the transmission dynamics of SARS-CoV-2. The authors considered three infectivity scenarios (basic reproduction number [R 0] of 1·5, 2·0, or 2·5) and assumed between 7·5% and 50·0% of infections were asymptomatic. The interventions were quarantine with or without school closure and workplace distancing (whereby 50% of workers telecommute). Although the complexity of the model makes it difficult to understand the impact of each parameter, the primary conclusions were robust to sensitivity analyses. The combined intervention, in which quarantine, school closure, and workplace distancing were implemented, was the most effective: compared with the baseline scenario of no interventions, the combined intervention reduced the estimated median number of infections by 99·3% (IQR 92·6–99·9) when R 0 was 1·5, by 93·0% (81·5–99·7) when R 0 was 2·0, and by 78·2% (59·0–94·4) when R 0 was 2·5. The observation that the greatest reduction in COVID-19 cases was achieved under the combined intervention is not surprising. However, the assessment of the additional benefit of each intervention, when implemented in combination, offers valuable insight. Since each approach individually will result in considerable societal disruption, it is important to understand the extent of intervention needed to reduce transmission and disease burden. New findings emerge daily about transmission routes and the clinical profile of SARS-CoV-2, including the substantially underestimated rate of infection among children. 8 The implications of such findings with regard to the authors' conclusions about school closure remain unclear. Additionally, reproductive number estimates for Singapore are not yet available. The authors estimated that 7·5% of infections are clinically asymptomatic, although data on the proportion of infections that are asymptomatic are scarce; as shown by Koo and colleagues in sensitivity analyses with higher asymptomatic proportions, this value will influence the effectiveness of social-distancing interventions. Additionally, the analysis assumes high compliance of the general population, which is not guaranteed. Although the scientific basis for these interventions might be robust, ethical considerations are multifaceted. 9 Importantly, political leaders must enact quarantine and social-distancing policies that do not bias against any population group. The legacies of social and economic injustices perpetrated in the name of public health have lasting repercussions. 10 Interventions might pose risks of reduced income and even job loss, disproportionately affecting the most disadvantaged populations: policies to lessen such risks are urgently needed. Special attention should be given to protections for vulnerable populations, such as homeless, incarcerated, older, or disabled individuals, and undocumented migrants. Similarly, exceptions might be necessary for certain groups, including people who are reliant on ongoing medical treatment. The effectiveness and societal impact of quarantine and social distancing will depend on the credibility of public health authorities, political leaders, and institutions. It is important that policy makers maintain the public's trust through use of evidence-based interventions and fully transparent, fact-based communication. © 2020 Caia Image/Science Photo Library 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
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                Author and article information

                Contributors
                william.waites@strath.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 May 2022
                20 May 2022
                2022
                : 12
                : 8550
                Affiliations
                [1 ]GRID grid.8991.9, ISNI 0000 0004 0425 469X, Centre for Mathematical Modelling of Infectious Diseases, , London School of Hygiene and Tropical Medicine, ; Keppel Street, London, UK
                [2 ]GRID grid.11984.35, ISNI 0000000121138138, Department of Computer and Information Sciences, , University of Strathclyde, ; Glasgow, Scotland, UK
                [3 ]GRID grid.8991.9, ISNI 0000 0004 0425 469X, Department of Clinical Research, , London School of Hygiene and Tropical Medicine, ; Keppel Street, London, UK
                [4 ]GRID grid.5379.8, ISNI 0000000121662407, School of Mathematics, , University of Manchester, ; Manchester, UK
                [5 ]GRID grid.83440.3b, ISNI 0000000121901201, Great Ormond Street Institute of Child Health Biomedical Research Centre, , University College London, ; London, UK
                [6 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, Centre for Health, Law and Society, , University of Bristol Law School, ; Bristol, UK
                [7 ]GRID grid.9619.7, ISNI 0000 0004 1937 0538, Department of Sociology and Anthropology, , Hebrew University of Jerusalem, ; Jerusalem, Israel
                [8 ]GRID grid.439749.4, ISNI 0000 0004 0612 2754, Hospital for Tropical Diseases, , University College London Hospital NHS Foundation Trust, ; London, UK
                [9 ]GRID grid.8991.9, ISNI 0000 0004 0425 469X, Department of Global Health and Development, , London School of Hygiene and Tropical Medicine, ; Keppel Street, London, UK
                Article
                12517
                10.1038/s41598-022-12517-6
                9121858
                51be6b77-6dc0-4b07-bb60-164d74703b25
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 September 2021
                : 12 May 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: MR/V027956/1
                Funded by: FundRef http://dx.doi.org/10.13039/100010269, Wellcome Trust;
                Funded by: LSHTM Alumni COVID-19 Response Fund
                Funded by: Health Data Research, UK
                Award ID: MR/S003975/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Categories
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                © The Author(s) 2022

                Uncategorized
                computational models,viral infection,epidemiology,computer science
                Uncategorized
                computational models, viral infection, epidemiology, computer science

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