Bayes’ Theorem represents a mathematical formalization of the common sense. What we know about the world today is what we knew yesterday plus what the data told us. The lack of understanding of this concept is the source of many errors and wrong judgements in the current COVID-19 pandemic. In this contribution, we show how to use the framework of Bayesian inference to produce a reasonable estimate of seroprevalence from studies that use a single binary test. Bayes’ Theorem sometimes produces results that seem counter-intuitive at first sight. It is important to realize that the reality may be different from its image represented by test results. The extent to which these two worlds differ depends on the performance of the test (i.e. its sensitivity and specificity),and the prevalence of the tested condition.
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Towards Bayesian evaluation of seroprevalence studies
Published: 11 January 2021 by MDPI in The 3rd International Electronic Conference on Environmental Research and Public Health —Public Health Issues in the Context of the COVID-19 Pandemic session Public Health Statistics and Risk Assessment
Keywords: Bayesian; seroprevalence; antibodies; false positive; SARS-CoV-2; COVID-19