Inferring the COVID-19 infection fatality rate in the community-dwelling population: a simple Bayesian evidence synthesis of seroprevalence study data and imprecise mortality data

Abstract

Estimating the COVID-19 infection fatality rate (IFR) has proven to be particularly challenging --and rather controversial-- due to the fact that both the data on deaths and the data on the number of individuals infected are subject to many different biases. We consider a Bayesian evidence synthesis approach which, while simple enough for researchers to understand and use, accounts for many important sources of uncertainty inherent in both the seroprevalence and mortality data. With the understanding that the results of one's evidence synthesis analysis may be largely driven by which studies are included and which are excluded, we conduct two separate parallel analyses based on two lists of eligible studies obtained from two different research teams. The results from both analyses are rather similar. With the first analysis, we estimate the COVID-19 IFR to be 0.37% (95% credible interval of (0.19%, 0.61%)) for a typical population where 9% of the population is aged over 65 years and where the GDP (at purchasing-power parity (PPP)) per capita is $17.8k (the approximate worldwide average). With the second analysis, we obtain 0.37% (95% credible interval of (0.22%, 0.55%)). Our results suggest that, as one might expect, lower IFRs are associated with younger populations (and may also be associated with wealthier populations). For a typical population with the age and wealth of the United States we estimate an IFR of 0.61% and 0.59%; and for a typical population with the age and wealth of the European Union, we obtain IFR estimates of 0.90% and 0.62%.