Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model
Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model
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London: Nature Publishing Group UK
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Language
English
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London: Nature Publishing Group UK
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The individual results of SARS-CoV-2 serological tests measured after the first pandemic wave of 2020 cannot be directly interpreted as a probability of having been infected. Plus, these results are usually returned as a binary or ternary variable, relying on predefined cut-offs. We propose a Bayesian mixture model to estimate individual infection...
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Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model
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TN_cdi_doaj_primary_oai_doaj_org_article_47bfca899a434bb0b52001d0eec3932c
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_47bfca899a434bb0b52001d0eec3932c
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-60060-3
