Inferring the COVID-19 IFR with a simple Bayesian evidence synthesis of seroprevalence study data and imprecise mortality data

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. O_QDAbove all, whats needed is humility in the face of an intricately evolving body of evidence. The pandemic could well drift or shift into something that defies our best efforts to model and characterize it. Siddhartha Mukherjee, The New Yorker February 22, 2021 C_QD