Towards Bayesian evaluation of seroprevalence studies

Abstract

Bayes’ Theorem represents a mathematical formalization of the common sense. What we 1 know about the world today is what we knew yesterday plus what the data told us. The lack of 2 understanding of this concept is the source of many errors and wrong judgements in the current 3 COVID-19 pandemic. In this contribution, we show how to use the framework of Bayesian inference 4 to produce a reasonable estimate of seroprevalence from studies that use a single binary test. Bayes’ 5 Theorem sometimes produces results that seem counter-intuitive at first sight. It is important to 6 realize that the reality may be different from its image represented by test results. The extent to which 7 these two worlds differ depends on the performance of the test (i.e. its sensitivity and specificity), 8 and the prevalence of the tested condition. 9