104
views
0
recommends
+1 Recommend
1 collections
    1
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Estimating the burden of SARS-CoV-2 in France

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          France has been heavily affected by the SARS-CoV-2 epidemic and went into lockdown on the 17 March 2020. Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity. We find 3.6% of infected individuals are hospitalized and 0.7% die, ranging from 0.001% in those <20 years of age (ya) to 10.1% in those >80ya. Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women. The lockdown reduced the reproductive number from 2.90 to 0.67 (77% reduction). By 11 May 2020, when interventions are scheduled to be eased, we project 2.8 million (range: 1.8–4.7) people, or 4.4% (range: 2.8–7.2) of the population, will have been infected. Population immunity appears insufficient to avoid a second wave if all control measures are released at the end of the lockdown.

          Related collections

          Most cited references12

          • Record: found
          • Abstract: found
          • Article: not found

          Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis

          Highlights • COVID -19 cases are now confirmed in multiple countries. • Assessed the prevalence of comorbidities in infected patients. • Comorbidities are risk factors for severe compared with non-severe patients. • Help the health sector guide vulnerable populations and assess the risk of deterioration.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              The effect of human mobility and control measures on the COVID-19 epidemic in China

              The ongoing COVID-19 outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions have been undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
                Bookmark

                Author and article information

                Journal
                Science
                Science
                SCIENCE
                Science (New York, N.y.)
                American Association for the Advancement of Science
                0036-8075
                1095-9203
                13 May 2020
                : eabc3517
                Affiliations
                [1 ]Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
                [2 ]Department of Genetics, University of Cambridge, Cambridge, UK.
                [3 ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
                [4 ]Collège Doctoral, Sorbonne Université, Paris, France.
                [5 ]DREES, Ministère des Solidarités et de la Santé, Paris, France.
                [6 ]Santé Publique France, French National Public Health Agency, Saint-Maurice, France.
                [7 ]Emerging Diseases Epidemiology Unit, Institut Pasteur, Paris, France.
                [8 ]PACRI Unit, Conservatoire National des Arts et Métiers, Paris, France.
                [9 ]Epidemiology and Modelling of Antibiotic Evasion Unit, Institut Pasteur, Paris, France.
                [10 ]Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France.
                [11 ]Institut Pierre Louis d’Epidémiologie et de Santé Publique, Sorbonne Université, INSERM, Paris, France.
                Author notes
                [*]

                These authors contributed equally to this work.

                []Corresponding author. Email: simon.cauchemez@ 123456pasteur.fr
                Author information
                https://orcid.org/0000-0003-3626-4254
                https://orcid.org/0000-0003-0563-8428
                https://orcid.org/0000-0001-9703-7851
                https://orcid.org/0000-0002-5143-6256
                https://orcid.org/0000-0002-5242-4281
                https://orcid.org/0000-0002-6180-4200
                https://orcid.org/0000-0002-9741-8109
                https://orcid.org/0000-0002-5367-8232
                https://orcid.org/0000-0001-9186-4549
                Article
                abc3517
                10.1126/science.abc3517
                7223792
                32404476
                b044a8b8-ce51-4192-8a3d-9f9fafb9d918
                Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 20 April 2020
                : 11 May 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: 804744
                Funded by: ANR;
                Award ID: ANR-10-LABX-62-IBEID
                Funded by: INCEPTION;
                Award ID: PIA/ANR-16-CONV-0005
                Funded by: Santé Publique France;
                Funded by: European Union RECOVER;
                Funded by: University of Cambridge COVID-19 Rapid Response Grant;
                Categories
                Report
                Reports
                Reports
                Epidemiology
                Custom metadata
                4
                4

                Uncategorized
                Uncategorized

                Comments

                Comment on this article