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

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-Wü rttemberg (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%

SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates

“…Both of these estimates are in agreement with the present study estimate of 3.7 (95% CI: 3.7-3.8) per 1,000 infections. These figures, however, are substantially lower than those estimated elsewhere, often using early epidemic data [24][25][26][27][28][29]. The fact that the early phases of the epidemic in Europe and the United States heavily affected nursing facilities and care homes of the elderly may have biased the estimates to higher values.…”

Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis

“…As of 30th of June 2020, COVID-19 has caused more than 500,000 deaths globally, with an estimated case fatality of 1–4% ( Hauser et al 2020 ). The UK is one of the countries most affected, with an estimated 57,300 more deaths in England and Wales than it would be expected from mid-February to end of May 2020 had the pandemic not taken place ( Kontis et al 2020 ).…”

Estimates of COVID-19 Cases across Four Canadian Provinces

“…Although these models require fewer assumptions regarding the underlying parameter values, because they scale up observed deaths to estimate population infections, small differences in the assumed case fatality will have substantial effects on the estimates. Given considerable uncertainty regarding the true case fatality, which may depend on local sociodemographic and environmental conditions, and the fact that COVID-19-related deaths may be undercounted during the course of the pandemic, these estimates may not capture the overall extent of population infection (see Severnini 2018 , 2019 ;Clay et al 2020 ;Han et al 2020 ;Katz and Sanger-Katz 2020 ;Prakash and Hall 2020 ;Riou, Hauser, Counotte, Margossian, et al 2020 ;Wu et al 2020 ).…”