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Journal Articles Scientific Reports Year : 2024

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

Benjamin Glemain (1) , Xavier de Lamballerie (2) , Marie Zins (3, 4) , Gianluca Severi (5, 6) , Mathilde Touvier (7) , Jean-François Deleuze (8) , Fabrice Carrat (1) , Pierre-Yves Ancel , Marie-Aline Charles , Gianluca Severi , Mathilde Touvier , Marie Zins , Sofiane Kab , Adeline Renuy , Stephane Le-Got , Celine Ribet , Mireille Pellicer , Emmanuel Wiernik , Marcel Goldberg , Fanny Artaud , Pascale Gerbouin-Rérolle , Mélody Enguix , Camille Laplanche , Roselyn Gomes-Rima , Lyan Hoang , Emmanuelle Correia , Alpha Amadou Barry , Nadège Senina , Julien Allegre , Fabien Szabo de Edelenyi , Nathalie Druesne-Pecollo , Younes Esseddik , Serge Hercberg , Mélanie Deschasaux , Marie-Aline Charles , Valérie Benhammou , Anass Ritmi , Laetitia Marchand , Cecile Zaros , Elodie Lordmi , Adriana Candea , Sophie de Visme , Thierry Simeon , Xavier Thierry , Bertrand Geay , Marie-Noelle Dufourg , Karen Milcent , Delphine Rahib , Nathalie Lydie , Clovis Lusivika-Nzinga , Gregory Pannetier , Nathanael Lapidus , Isabelle Goderel , Céline Dorival , Jérôme Nicol , Olivier Robineau , Cindy Lai , Liza Belhadji , Hélène Esperou , Sandrine Couffin-Cadiergues , Jean-Marie Gagliolo , Hélène Blanché , Jean-Marc Sébaoun , Jean-Christophe Beaudoin , Laetitia Gressin , Valérie Morel , Ouissam Ouili , Jean-François Deleuze , Laetitia Ninove , Stéphane Priet , Paola Mariela Saba Villarroel , Toscane Fourié , Souand Mohamed Ali , Abdenour Amroun , Morgan Seston , Nazli Ayhan , Boris Pastorino , Xavier de Lamballerie , Nathanaël Lapidus , Fabrice Carrat
Pierre-Yves Ancel
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Marie-Aline Charles
  • Function : Author
Gianluca Severi
  • Function : Author
Mathilde Touvier
  • Function : Author
Marie Zins
  • Function : Author
Sofiane Kab
  • Function : Author
Adeline Renuy
  • Function : Author
Stephane Le-Got
  • Function : Author
Celine Ribet
  • Function : Author
Mireille Pellicer
  • Function : Author
Emmanuel Wiernik
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Marcel Goldberg
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Fanny Artaud
  • Function : Author
Pascale Gerbouin-Rérolle
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Mélody Enguix
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Camille Laplanche
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Roselyn Gomes-Rima
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Lyan Hoang
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Emmanuelle Correia
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Alpha Amadou Barry
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Nadège Senina
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Julien Allegre
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Fabien Szabo de Edelenyi
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Nathalie Druesne-Pecollo
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Younes Esseddik
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Serge Hercberg
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Mélanie Deschasaux
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Marie-Aline Charles
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Valérie Benhammou
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Anass Ritmi
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Laetitia Marchand
  • Function : Author
Cecile Zaros
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Elodie Lordmi
  • Function : Author
Adriana Candea
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Sophie de Visme
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Thierry Simeon
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Xavier Thierry
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Bertrand Geay
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Marie-Noelle Dufourg
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Karen Milcent
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Delphine Rahib
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Nathalie Lydie
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Clovis Lusivika-Nzinga
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Gregory Pannetier
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Nathanael Lapidus
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Isabelle Goderel
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Céline Dorival
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Jérôme Nicol
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Olivier Robineau
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Cindy Lai
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Liza Belhadji
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Hélène Esperou
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Sandrine Couffin-Cadiergues
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Jean-Marie Gagliolo
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Hélène Blanché
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Jean-Marc Sébaoun
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Jean-Christophe Beaudoin
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Laetitia Gressin
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Valérie Morel
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Ouissam Ouili
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Jean-François Deleuze
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Laetitia Ninove
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Stéphane Priet
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Paola Mariela Saba Villarroel
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Toscane Fourié
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Souand Mohamed Ali
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Abdenour Amroun
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Morgan Seston
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Nazli Ayhan
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Boris Pastorino
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Xavier de Lamballerie
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Nathanaël Lapidus
  • Function : relator_co_last_author
Fabrice Carrat
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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.
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hal-04561282 , version 1 (26-04-2024)

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Benjamin Glemain, Xavier de Lamballerie, Marie Zins, Gianluca Severi, Mathilde Touvier, et al.. Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model. Scientific Reports, 2024, 14 (1), pp.9503. ⟨10.1038/s41598-024-60060-3⟩. ⟨hal-04561282⟩
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