Skip to content
Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: a modelling study in Hubei, China and six locations of Europe

Anthony Hauser^a , Michel J. Counotte^a , Charles C. Margossian^b , Garyfallos Konstantinoudis^c , Nicola Low^a , Christian L. Althaus^a , and Julien Riou^(a,*)

^a Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland

^b Department of Statistics, Columbia University, New York, NY

^c MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

^* Corresponding author (


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 to understand clinical prognosis, plan health care capacity and for epidemic forecasting. The case fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but 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; 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: 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 80+ year olds, 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.

Figure. (A) Case fatality ratio, symptomatic case fatality ratio and infection fatality ratio estimates by geographic location. (B) Infection fatality ratio estimates by age group and location (for Austria, the estimates are adapted to the available age groups from 0-4 to 75+ years). (C) Proportion of cases ascertained by age group and location (color code as for panel B). (D) Distribution of reported cases by age group by location (color code as for panel B).


No description, website, or topics provided.




No releases published


No packages published