Open Access
March 2022 Bayesian adjustment for preferential testing in estimating infection fatality rates, as motivated by the COVID-19 pandemic
Harlan Campbell, Perry de Valpine, Lauren Maxwell, Valentijn M. T. de Jong, Thomas P. A. Debray, Thomas Jaenisch, Paul Gustafson
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Ann. Appl. Stat. 16(1): 436-459 (March 2022). DOI: 10.1214/21-AOAS1499

Abstract

A key challenge in estimating the infection fatality rate (IFR), along with its relation with various factors of interest, is determining the total number of cases. The total number of cases is not known not only because not everyone is tested but also, more importantly, because tested individuals are not representative of the population at large. We refer to the phenomenon whereby infected individuals are more likely to be tested than noninfected individuals as “preferential testing.” An open question is whether or not it is possible to reliably estimate the IFR without any specific knowledge about the degree to which the data are biased by preferential testing. In this paper we take a partial identifiability approach, formulating clearly where deliberate prior assumptions can be made and presenting a Bayesian model which pools information from different samples. When the model is fit to European data obtained from seroprevalence studies and national official COVID-19 statistics, we estimate the overall COVID-19 IFR for Europe to be 0.53%, 95% C.I.=[0.38%,0.70%].

Funding Statement

This work was supported by the European Union’s Horizon 2020 research and innovation programme under ReCoDID grant agreement No. 825746 and by the Canadian Institutes of Health Research, Institute of Genetics (CIHR-IG) under Grant Agreement No. 01886-000.

Acknowledgments

We wish to thank Joe Watson for his input early on and expertise on preferential sampling.

Citation

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Harlan Campbell. Perry de Valpine. Lauren Maxwell. Valentijn M. T. de Jong. Thomas P. A. Debray. Thomas Jaenisch. Paul Gustafson. "Bayesian adjustment for preferential testing in estimating infection fatality rates, as motivated by the COVID-19 pandemic." Ann. Appl. Stat. 16 (1) 436 - 459, March 2022. https://doi.org/10.1214/21-AOAS1499

Information

Received: 1 October 2020; Revised: 1 May 2021; Published: March 2022
First available in Project Euclid: 28 March 2022

MathSciNet: MR4400517
zbMATH: 1498.62196
Digital Object Identifier: 10.1214/21-AOAS1499

Keywords: evidence synthesis , partial identification , selection bias

Rights: Copyright © 2022 Institute of Mathematical Statistics

Vol.16 • No. 1 • March 2022
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