학술논문
Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model.
Document Type
Article
Author
Glemain, Benjamin; de Lamballerie, Xavier; Zins, Marie; Severi, Gianluca; Touvier, Mathilde; Deleuze, Jean-François; Carrat, Fabrice; Ancel, Pierre-Yves; Charles, Marie-Aline; Kab, Sofiane; Renuy, Adeline; Le-Got, Stephane; Ribet, Celine; Pellicer, Mireille; Wiernik, Emmanuel; Goldberg, Marcel; Artaud, Fanny; Gerbouin-Rérolle, Pascale; Enguix, Mélody; Laplanche, Camille
Source
Subject
*PROBABILITY theory
*SARS-CoV-2
*BAYES' theorem
*ESTIMATES
*SERODIAGNOSIS
*COVID-19 pandemic
*INFECTION
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Language
ISSN
2045-2322
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 a Bayesian mixture model to estimate individual infection probabilities, based on 81,797 continuous anti-spike IgG tests from Euroimmun collected in France after the first wave. This approach used serological results as a continuous variable, and was therefore not based on diagnostic cut-offs. Cumulative incidence, which is necessary to compute infection probabilities, was estimated according to age and administrative region. In France, we found that a "negative" or a "positive" test, as classified by the manufacturer, could correspond to a probability of infection as high as 61.8% or as low as 67.7%, respectively. "Indeterminate" tests encompassed probabilities of infection ranging from 10.8 to 96.6%. Our model estimated tailored individual probabilities of SARS-CoV-2 infection based on age, region, and serological result. It can be applied in other contexts, if estimates of cumulative incidence are available. [ABSTRACT FROM AUTHOR]