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
1
iPLESP -
Institut Pierre Louis d'Epidémiologie et de Santé Publique
2 IHU Marseille - Institut Hospitalier Universitaire Méditerranée Infection
3 Université Paris-Saclay
4 UVSQ - Université de Versailles Saint-Quentin-en-Yvelines
5 CESP - Centre de recherche en épidémiologie et santé des populations
6 UniFI - Università degli Studi di Firenze = University of Florence = Université de Florence
7 EREN [CRESS - U1153 / UMR_A 1125] - Nutritional Epidemiology Research Team | Equipe de Recherche en Epidémiologie Nutritionnelle
8 CEPH - Fondation Jean Dausset - Centre d’Etudes du Polymorphisme Humain [Paris]
2 IHU Marseille - Institut Hospitalier Universitaire Méditerranée Infection
3 Université Paris-Saclay
4 UVSQ - Université de Versailles Saint-Quentin-en-Yvelines
5 CESP - Centre de recherche en épidémiologie et santé des populations
6 UniFI - Università degli Studi di Firenze = University of Florence = Université de Florence
7 EREN [CRESS - U1153 / UMR_A 1125] - Nutritional Epidemiology Research Team | Equipe de Recherche en Epidémiologie Nutritionnelle
8 CEPH - Fondation Jean Dausset - Centre d’Etudes du Polymorphisme Humain [Paris]
Benjamin Glemain
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Pierre-Yves Ancel
- Function : Author
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
- Function : Author
Marcel Goldberg
- Function : Author
Fanny Artaud
- Function : Author
Pascale Gerbouin-Rérolle
- Function : Author
Mélody Enguix
- Function : Author
Camille Laplanche
- Function : Author
Roselyn Gomes-Rima
- Function : Author
Lyan Hoang
- Function : Author
Emmanuelle Correia
- Function : Author
Alpha Amadou Barry
- Function : Author
Nadège Senina
- Function : Author
Julien Allegre
- Function : Author
Fabien Szabo de Edelenyi
- Function : Author
Nathalie Druesne-Pecollo
- Function : Author
Younes Esseddik
- Function : Author
Serge Hercberg
- Function : Author
Mélanie Deschasaux
- Function : Author
Marie-Aline Charles
- Function : Author
Valérie Benhammou
- Function : Author
Anass Ritmi
- Function : Author
Laetitia Marchand
- Function : Author
Cecile Zaros
- Function : Author
Elodie Lordmi
- Function : Author
Adriana Candea
- Function : Author
Sophie de Visme
- Function : Author
Thierry Simeon
- Function : Author
Xavier Thierry
- Function : Author
Bertrand Geay
- Function : Author
Marie-Noelle Dufourg
- Function : Author
Karen Milcent
- Function : Author
Delphine Rahib
- Function : Author
Nathalie Lydie
- Function : Author
Clovis Lusivika-Nzinga
- Function : Author
Gregory Pannetier
- Function : Author
Nathanael Lapidus
- Function : Author
Isabelle Goderel
- Function : Author
Céline Dorival
- Function : Author
Jérôme Nicol
- Function : Author
Olivier Robineau
- Function : Author
Cindy Lai
- Function : Author
Liza Belhadji
- Function : Author
Hélène Esperou
- Function : Author
Sandrine Couffin-Cadiergues
- Function : Author
Jean-Marie Gagliolo
- Function : Author
Hélène Blanché
- Function : Author
Jean-Marc Sébaoun
- Function : Author
Jean-Christophe Beaudoin
- Function : Author
Laetitia Gressin
- Function : Author
Valérie Morel
- Function : Author
Ouissam Ouili
- Function : Author
Jean-François Deleuze
- Function : Author
Laetitia Ninove
- Function : Author
Stéphane Priet
- Function : Author
Paola Mariela Saba Villarroel
- Function : Author
Toscane Fourié
- Function : Author
Souand Mohamed Ali
- Function : Author
Abdenour Amroun
- Function : Author
Morgan Seston
- Function : Author
Nazli Ayhan
- Function : Author
Boris Pastorino
- Function : Author
Xavier de Lamballerie
- Function : Author
Nathanaël Lapidus
- Function : relator_co_last_author
Fabrice Carrat
- Function : relator_co_last_author
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.
Origin : Publication funded by an institution
licence : CC BY - Attribution
licence : CC BY - Attribution