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Proceeding Paper

Towards Bayesian Evaluation of Seroprevalence Studies †

by 1,*,‡, 2,‡, 3,‡, 4,‡ and 3,‡
1
Olomouc University Social Health Institute, Palacky University Olomouc, 77900 Olomouc, Czech Republic
2
Immunology Laboratory GENNET, 17000 Prague, Czech Republic
3
Department of Mathematical Analysis and Application of Mathematics, Faculty of Science, Palacky University Olomouc, 77900 Olomouc, Czech Republic
4
Department of Pharmacology, Faculty of Medicine and Dentistry, Palacky University Olomouc, 77515 Olomouc, Czech Republic
*
Author to whom correspondence should be addressed.
Presented at the 3rd International Electronic Conference on Environmental Research and Public Health—Public Health Issues in the Context of the COVID-19 Pandemic, 11–25 January 2021; Available online: https://ecerph-3.sciforum.net/.
The Center for Bayesian Inference 4BIN, www.4bin.org.
Academic Editor: Jon Øyvind Odland
Med. Sci. Forum 2021, 4(1), 11; https://doi.org/10.3390/ECERPH-3-09006
Published: 11 January 2021
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. View Full-Text
Keywords: Bayesian; seroprevalence; antibodies; false positive; SARS-CoV-2; COVID-19 Bayesian; seroprevalence; antibodies; false positive; SARS-CoV-2; COVID-19
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MDPI and ACS Style

Furstova, J.; Kratka, Z.; Furst, T.; Strojil, J.; Vencalek, O. Towards Bayesian Evaluation of Seroprevalence Studies. Med. Sci. Forum 2021, 4, 11. https://doi.org/10.3390/ECERPH-3-09006

AMA Style

Furstova J, Kratka Z, Furst T, Strojil J, Vencalek O. Towards Bayesian Evaluation of Seroprevalence Studies. Medical Sciences Forum. 2021; 4(1):11. https://doi.org/10.3390/ECERPH-3-09006

Chicago/Turabian Style

Furstova, Jana, Zuzana Kratka, Tomas Furst, Jan Strojil, and Ondrej Vencalek. 2021. "Towards Bayesian Evaluation of Seroprevalence Studies" Medical Sciences Forum 4, no. 1: 11. https://doi.org/10.3390/ECERPH-3-09006

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