About: Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe

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About: Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe Goto Sponge NotDistinct Permalink

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe
Creator	Riou, Julien Low, Nicola Althaus, Christian Counotte, Michel Hauser, Anthony Konstantinoudis, Garyfallos Margossian, Charles
source	Medline; PMC
abstract	BACKGROUND: As of 16 May 2020, more than 4.5 million cases and more than 300,000 deaths from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. Reliable estimates of mortality from SARS-CoV-2 infection are essential for understanding clinical prognosis, planning healthcare capacity, and epidemic forecasting. The case–fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but it can be a misleading measure of overall mortality. The objectives of this study were to (1) simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2 mortality adjusted for biases and examine the CFR, the symptomatic case–fatality ratio (sCFR), and the infection–fatality ratio (IFR) in different geographic locations. METHOD AND FINDINGS: We developed an age-stratified susceptible-exposed-infected-removed (SEIR) compartmental model describing the dynamics of transmission and mortality during the SARS-CoV-2 epidemic. Our model accounts for two biases: preferential ascertainment of severe cases and right-censoring of mortality. We fitted the transmission model to surveillance data from Hubei Province, China, and applied the same model to six regions in Europe: Austria, Bavaria (Germany), Baden-Württemberg (Germany), Lombardy (Italy), Spain, and Switzerland. In Hubei, the baseline estimates were as follows: CFR 2.4% (95% credible interval [CrI] 2.1%–2.8%), sCFR 3.7% (3.2%–4.2%), and IFR 2.9% (2.4%–3.5%). Estimated measures of mortality changed over time. Across the six locations in Europe, estimates of CFR varied widely. Estimates of sCFR and IFR, adjusted for bias, were more similar to each other but still showed some degree of heterogeneity. Estimates of IFR ranged from 0.5% (95% CrI 0.4%–0.6%) in Switzerland to 1.4% (1.1%–1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%–26%) in Switzerland to 34% (95% CrI 28%–40%) in Spain. A limitation of the model is that count data by date of onset are required, and these are not available in all countries. CONCLUSIONS: We propose a comprehensive solution to the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks. The CFR is not a good predictor of overall mortality from SARS-CoV-2 and should not be used for evaluation of policy or comparison across settings. Geographic differences in IFR suggest that a single IFR should not be applied to all settings to estimate the total size of the SARS-CoV-2 epidemic in different countries. The sCFR and IFR, adjusted for right-censoring and preferential ascertainment of severe cases, are measures that can be used to improve and monitor clinical and public health strategies to reduce the deaths from SARS-CoV-2 infection.
has issue date	2020-07-28(xsd:dateTime)
bibo:doi	10.1371/journal.pmed.1003189
bibo:pmid	32722715
has license	cc-by
sha1sum (hex)	a2a56c5afc2f70f775b39bd8ffd50e7d3c1243a0
schema:url	https://doi.org/10.1371/journal.pmed.1003189
resource representing a document's title	Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe
has PubMed Central identifier	PMC7386608
has PubMed identifier	32722715
schema:publication	PLoS Med
resource representing a document's body	covid:a2a56c5afc2f70f775b39bd8ffd50e7d3c1243a0#body_text
is schema:about of	named entity 'SARS-CoV-2' named entity 'China' named entity 'SARS-CoV-2' named entity 'Europe' named entity 'SARS-CoV-2' named entity 'Bavaria' named entity 'SARS-CoV-2' named entity 'fatality rate' named entity 'Hubei' named entity 'force of infection' named entity 'symptom' named entity 'prior distribution' named entity 'standard deviation' named entity 'SARS-CoV-2' named entity 'S15' named entity 'credible intervals' named entity 'symptom' named entity 'missing at random' named entity 'infection' named entity 'Baden-Württemberg' named entity 'log-normal distribution' named entity 'symptom' named entity 'cumulative incidence' named entity 'SARS-CoV-2' named entity 'vector' named entity 'SARS-CoV-2' named entity 'asymptomatic' named entity 'symptom' named entity 'Probability' named entity 'logistic function' named entity 'forcing function' named entity '37 days' named entity 'K-1' named entity 'Shanghai' named entity 'SARS-CoV-2' named entity 'missing at random' named entity 'symptom' named entity 'S26' named entity 'Hubei' named entity 'Bavaria' named entity 'Lombardy' named entity 'credible intervals' named entity 'symptom' named entity 'vector' named entity 'logistic function' named entity 'infection' named entity 'asymptomatic' named entity 'symptom' named entity 'incubation period' named entity 'China' named entity 'IFR' named entity 'asymptomatic' named entity 'infection' named entity 'asymptomatic' named entity 'general population' named entity 'missing at random' named entity 'incubation period' named entity '95% confidence interval' named entity 'epidemic' named entity 'infection' named entity 'Germany' named entity 'epidemic' named entity 'Hubei' named entity 'Italy' named entity 'infection fatality rate' named entity 'SEIR' named entity 'sensitivity analysis' named entity 'gt-1' named entity 'S25' named entity '27 January' named entity '11 February' named entity 'symptom' named entity 'credible intervals' named entity 'Shanghai' named entity 'SARS-CoV-2'

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