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. We estimate the COVID-19 IFR to be 0.38% (95% prediction interval of (0.03%, 1.19%)) for a typical population where the proportion of those aged over 65 years old is 9% (the approximate worldwide value). Our results suggest that, despite immense efforts made to better understand the COVID-19 IFR, there remains a large amount of uncertainty and unexplained heterogeneity surrounding this important statistic.
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
