Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model

Abstract 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...

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Auteurs principaux:	Benjamin Glemain, Xavier de Lamballerie, Marie Zins, Gianluca Severi, Mathilde Touvier, Jean-François Deleuze, SAPRIS-SERO study group, Nathanaël Lapidus, Fabrice Carrat
Format:	Artigo
Langue:	Inglês
Publié:	Nature Portfolio 2024-04-01
Collection:	Scientific Reports
Sujets:	SARS-CoV-2 COVID-19 Bayes’ theorem Mixture model
Accès en ligne:	https://doi.org/10.1038/s41598-024-60060-3
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