Bayesian adjustment for preferential testing in estimating the COVID-19 infection fatality rate

TL;DR

This paper introduces a Bayesian method to estimate COVID-19 infection fatality rate (IFR) accounting for preferential testing bias, providing more reliable estimates from biased samples.

Contribution

It develops a Bayesian model that incorporates prior assumptions and pools data to address preferential testing bias in IFR estimation.

Findings

Estimated European COVID-19 IFR at 0.53% with 95% confidence interval

Demonstrated the model's ability to handle biased testing data

Provided a framework for partial identifiability in epidemiological estimates

Abstract

A key challenge in estimating the infection fatality rate (IFR) -- and its relation with various factors of interest -- is determining the total number of cases. The total number of cases is not known 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 non-infected 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. = [0.39%, 0.69%].