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      Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study

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      , PhD a , i , p , * , , MS m , , , PhD a , q , , , PhD m , r , , , PhD r , , PhD i , , MSc i , , PhD a , p , , PhD a , , MSc a , , MSc b , c , , MSc c , , Prof, PhD m , , Prof, MD n , , Prof, MD d , , Prof, MD s , , Prof, MD e , , MD f , i , , Prof, MD g , i , , Prof, MD c , j , , Prof, MD b , l , , PhD k , o , , Prof, MD g , i , m , , Prof, MD c , h , l , , Prof, PhD a , i
      Lancet (London, England)
      Elsevier Ltd.

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          Summary

          Background

          Assessing the burden of COVID-19 on the basis of medically attended case numbers is suboptimal given its reliance on testing strategy, changing case definitions, and disease presentation. Population-based serosurveys measuring anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) antibodies provide one method for estimating infection rates and monitoring the progression of the epidemic. Here, we estimate weekly seroprevalence of anti-SARS-CoV-2 antibodies in the population of Geneva, Switzerland, during the epidemic.

          Methods

          The SEROCoV-POP study is a population-based study of former participants of the Bus Santé study and their household members. We planned a series of 12 consecutive weekly serosurveys among randomly selected participants from a previous population-representative survey, and their household members aged 5 years and older. We tested each participant for anti-SARS-CoV-2-IgG antibodies using a commercially available ELISA. We estimated seroprevalence using a Bayesian logistic regression model taking into account test performance and adjusting for the age and sex of Geneva's population. Here we present results from the first 5 weeks of the study.

          Findings

          Between April 6 and May 9, 2020, we enrolled 2766 participants from 1339 households, with a demographic distribution similar to that of the canton of Geneva. In the first week, we estimated a seroprevalence of 4·8% (95% CI 2·4–8·0, n=341). The estimate increased to 8·5% (5·9–11·4, n=469) in the second week, to 10·9% (7·9–14·4, n=577) in the third week, 6·6% (4·3–9·4, n=604) in the fourth week, and 10·8% (8·2–13·9, n=775) in the fifth week. Individuals aged 5–9 years (relative risk [RR] 0·32 [95% CI 0·11–0·63]) and those older than 65 years (RR 0·50 [0·28–0·78]) had a significantly lower risk of being seropositive than those aged 20–49 years. After accounting for the time to seroconversion, we estimated that for every reported confirmed case, there were 11·6 infections in the community.

          Interpretation

          These results suggest that most of the population of Geneva remained uninfected during this wave of the pandemic, despite the high prevalence of COVID-19 in the region (5000 reported clinical cases over <2·5 months in the population of half a million people). Assuming that the presence of IgG antibodies is associated with immunity, these results highlight that the epidemic is far from coming to an end by means of fewer susceptible people in the population. Further, a significantly lower seroprevalence was observed for children aged 5–9 years and adults older than 65 years, compared with those aged 10–64 years. These results will inform countries considering the easing of restrictions aimed at curbing transmission.

          Funding

          Swiss Federal Office of Public Health, Swiss School of Public Health (Corona Immunitas research program), Fondation de Bienfaisance du Groupe Pictet, Fondation Ancrage, Fondation Privée des Hôpitaux Universitaires de Genève, and Center for Emerging Viral Diseases.

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          Most cited references16

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          Estimates of the severity of coronavirus disease 2019: a model-based analysis

          Summary Background In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. Methods We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. Findings Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–7·6) in those aged 80 years or older. Interpretation These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. Funding UK Medical Research Council.
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            Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

            Intense non-pharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact surveys data for Wuhan and Shanghai before and during the outbreak and contact tracing information from Hunan Province. Daily contacts were reduced 7-8-fold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0-14 years are less susceptible to SARS-CoV-2 infection than adults 15-64 years of age (odd ratio 0.34, 95%CI 0.24-0.49), while in contrast, individuals over 65 years are more susceptible to infection (odd ratio 1.47, 95%CI: 1.12-1.92). Based on these data, we build a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. While proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40-60% and delay the epidemic.
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              Defining the Epidemiology of Covid-19 — Studies Needed

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                Author and article information

                Contributors
                Journal
                Lancet
                Lancet
                Lancet (London, England)
                Elsevier Ltd.
                0140-6736
                1474-547X
                11 June 2020
                11 June 2020
                Affiliations
                [a ]Division of Primary Care, Geneva University Hospitals, Geneva, Switzerland
                [b ]Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
                [c ]Geneva Center for Emerging Viral Diseases and Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland
                [d ]Division of General Pediatrics, Geneva University Hospitals, Geneva, Switzerland
                [e ]Infection Prevention and Control Program and World Health Organization Collaborating Centre on Patient Safety, Geneva University Hospitals, Geneva, Switzerland
                [f ]Division of Penitentiary Medicine, Geneva University Hospitals, Geneva, Switzerland
                [g ]Division of Tropical and Humanitarian Medicine, Geneva University Hospitals, Geneva, Switzerland
                [h ]Division of Infectious Diseases, Geneva University Hospitals, Geneva, Switzerland
                [i ]Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
                [j ]Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
                [k ]Department of Pathology and Immunology, Center for Vaccinology, Faculty of Medicine, University of Geneva, Geneva, Switzerland
                [l ]Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
                [m ]Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
                [n ]Institut Ethique, Histoire, Humanités, University of Geneva, Geneva, Switzerland
                [o ]Centre for Vaccinology, Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland
                [p ]University Centre for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
                [q ]Faculty of BioMedicine, Università della Svizzera Italiana, Lugano, Switzerland
                [r ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
                [s ]School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
                Author notes
                [* ]Correspondence to: Dr Silvia Stringhini, Division of Primary Care, Geneva University Hospitals, 1205 Geneva, Switzerland silvia.stringhini@ 123456hcuge.ch
                [†]

                Contributed equally

                Article
                S0140-6736(20)31304-0
                10.1016/S0140-6736(20)31304-0
                7289564
                32534626
                36f2c2f3-e96f-442e-bfa6-25b829e0e9ea
                © 2020 Elsevier Ltd. All rights reserved.

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