Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model.
Humans
COVID-19
/ epidemiology
Bayes Theorem
SARS-CoV-2
/ immunology
Middle Aged
Adult
France
/ epidemiology
Aged
Antibodies, Viral
/ blood
Probability
Immunoglobulin G
/ blood
Adolescent
Female
COVID-19 Serological Testing
/ methods
Young Adult
Male
Incidence
Child
Child, Preschool
Infant
Aged, 80 and over
Bayes’ theorem
COVID-19
Mixture model
SARS-CoV-2
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
25 04 2024
25 04 2024
Historique:
received:
21
09
2023
accepted:
18
04
2024
medline:
26
4
2024
pubmed:
26
4
2024
entrez:
25
4
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38664455
doi: 10.1038/s41598-024-60060-3
pii: 10.1038/s41598-024-60060-3
doi:
Substances chimiques
Antibodies, Viral
0
Immunoglobulin G
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
9503Subventions
Organisme : Agence Nationale de la Recherche
ID : ANR-10-COHO-06
Organisme : Fondation pour la Recherche Médicale
ID : 20RR052-00
Organisme : Institut National de la Santé et de la Recherche Médicale
ID : C20-26
Investigateurs
Fabrice Carrat
(F)
Pierre-Yves Ancel
(PY)
Marie-Aline Charles
(MA)
Gianluca Severi
(G)
Mathilde Touvier
(M)
Marie Zins
(M)
Sofiane Kab
(S)
Adeline Renuy
(A)
Stephane Le-Got
(S)
Celine Ribet
(C)
Mireille Pellicer
(M)
Emmanuel Wiernik
(E)
Marcel Goldberg
(M)
Fanny Artaud
(F)
Pascale Gerbouin-Rérolle
(P)
Mélody Enguix
(M)
Camille Laplanche
(C)
Roselyn Gomes-Rima
(R)
Lyan Hoang
(L)
Emmanuelle Correia
(E)
Alpha Amadou Barry
(AA)
Nadège Senina
(N)
Julien Allegre
(J)
Fabien Szabo de Edelenyi
(F)
Nathalie Druesne-Pecollo
(N)
Younes Esseddik
(Y)
Serge Hercberg
(S)
Mélanie Deschasaux
(M)
Marie-Aline Charles
(MA)
Valérie Benhammou
(V)
Anass Ritmi
(A)
Laetitia Marchand
(L)
Cecile Zaros
(C)
Elodie Lordmi
(E)
Adriana Candea
(A)
Sophie de Visme
(S)
Thierry Simeon
(T)
Xavier Thierry
(X)
Bertrand Geay
(B)
Marie-Noelle Dufourg
(MN)
Karen Milcent
(K)
Delphine Rahib
(D)
Nathalie Lydie
(N)
Clovis Lusivika-Nzinga
(C)
Gregory Pannetier
(G)
Nathanael Lapidus
(N)
Isabelle Goderel
(I)
Céline Dorival
(C)
Jérôme Nicol
(J)
Olivier Robineau
(O)
Cindy Lai
(C)
Liza Belhadji
(L)
Hélène Esperou
(H)
Sandrine Couffin-Cadiergues
(S)
Jean-Marie Gagliolo
(JM)
Hélène Blanché
(H)
Jean-Marc Sébaoun
(JM)
Jean-Christophe Beaudoin
(JC)
Laetitia Gressin
(L)
Valérie Morel
(V)
Ouissam Ouili
(O)
Jean-François Deleuze
(JF)
Laetitia Ninove
(L)
Stéphane Priet
(S)
Paola Mariela Saba Villarroel
(PMS)
Toscane Fourié
(T)
Souand Mohamed Ali
(SM)
Abdenour Amroun
(A)
Morgan Seston
(M)
Nazli Ayhan
(N)
Boris Pastorino
(B)
Xavier de Lamballerie
(X)
Informations de copyright
© 2024. The Author(s).
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