Towards Bayesian Evaluation of Seroprevalence Studies
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 c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Medical Sciences Forum |
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| Online Access: | https://www.mdpi.com/2673-9992/4/1/11 |
| _version_ | 1797593244658302976 |
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| author | Jana Furstova Zuzana Kratka Tomas Furst Jan Strojil Ondrej Vencalek |
| author_facet | Jana Furstova Zuzana Kratka Tomas Furst Jan Strojil Ondrej Vencalek |
| author_sort | Jana Furstova |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T02:06:18Z |
| format | Article |
| id | doaj.art-6486a9c78ed547268d3e8d234a7b2550 |
| institution | Directory Open Access Journal |
| issn | 2673-9992 |
| language | English |
| last_indexed | 2024-03-11T02:06:18Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Medical Sciences Forum |
| spelling | doaj.art-6486a9c78ed547268d3e8d234a7b25502023-11-18T11:52:00ZengMDPI AGMedical Sciences Forum2673-99922021-01-01411110.3390/ECERPH-3-09006Towards Bayesian Evaluation of Seroprevalence StudiesJana Furstova0Zuzana Kratka1Tomas Furst2Jan Strojil3Ondrej Vencalek4Olomouc University Social Health Institute, Palacky University Olomouc, 77900 Olomouc, Czech RepublicImmunology Laboratory GENNET, 17000 Prague, Czech RepublicDepartment of Mathematical Analysis and Application of Mathematics, Faculty of Science, Palacky University Olomouc, 77900 Olomouc, Czech RepublicDepartment of Pharmacology, Faculty of Medicine and Dentistry, Palacky University Olomouc, 77515 Olomouc, Czech RepublicDepartment of Mathematical Analysis and Application of Mathematics, Faculty of Science, Palacky University Olomouc, 77900 Olomouc, Czech RepublicBayes’ 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.https://www.mdpi.com/2673-9992/4/1/11Bayesianseroprevalenceantibodiesfalse positiveSARS-CoV-2COVID-19 |
| spellingShingle | Jana Furstova Zuzana Kratka Tomas Furst Jan Strojil Ondrej Vencalek Towards Bayesian Evaluation of Seroprevalence Studies Medical Sciences Forum Bayesian seroprevalence antibodies false positive SARS-CoV-2 COVID-19 |
| title | Towards Bayesian Evaluation of Seroprevalence Studies |
| title_full | Towards Bayesian Evaluation of Seroprevalence Studies |
| title_fullStr | Towards Bayesian Evaluation of Seroprevalence Studies |
| title_full_unstemmed | Towards Bayesian Evaluation of Seroprevalence Studies |
| title_short | Towards Bayesian Evaluation of Seroprevalence Studies |
| title_sort | towards bayesian evaluation of seroprevalence studies |
| topic | Bayesian seroprevalence antibodies false positive SARS-CoV-2 COVID-19 |
| url | https://www.mdpi.com/2673-9992/4/1/11 |
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