Estimating the SARS-CoV-2 infection fatality rate by data combination: the case of Germany’s first wave
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Assessing the infection fatality rate (IFR) of SARS-CoV-2 in a population is a controversial issue. Due to asymptomatic courses of COVID-19, many infections remain undetected. Reported case fatality rates are therefore poor estimates of the IFR. We propose a strategy to estimate the IFR that combines official data on cases and fatalities with data from seroepidemiological studies in infection hotspots. The application of the method yields an estimate of the IFR of wild-type SARS-CoV-2 in Germany during the first wave of the pandemic of 0.83% (95% CI: [0.69%; 0.98%]), notably higher than the estimate reported in the prominent study by Streeck et al. (2020) (0.36% [0.17%; 0.77%]) and closer to that obtained from a world-wide meta analysis (0.68% [0.53%; 0.82%]), where the difference can be explained by Germany’s disadvantageous age structure. Provided that suitable data are available, the proposed method can be applied to estimate the IFR of virus variants and other regions.
Article activity feed
-
-
SciScore for 10.1101/2021.01.26.21250507: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Empirical implementation: The empirical analysis is performed in Matlab®, Version 2020b. Matlab®suggested: (MATLAB, RRID:SCR_001622)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any …
SciScore for 10.1101/2021.01.26.21250507: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Empirical implementation: The empirical analysis is performed in Matlab®, Version 2020b. Matlab®suggested: (MATLAB, RRID:SCR_001622)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
