Application of Machine Learning Prediction of Individual SARS-CoV-2 Vaccination and Infection Status to the French Serosurveillance Survey From March 2020 to 2022: Cross-Sectional Study - Anses - Agence nationale de sécurité sanitaire de l’alimentation, de l’environnement et du travail Accéder directement au contenu
Article Dans Une Revue JMIR Public Health and Surveillance Année : 2023

Application of Machine Learning Prediction of Individual SARS-CoV-2 Vaccination and Infection Status to the French Serosurveillance Survey From March 2020 to 2022: Cross-Sectional Study

Mikael Attia
Jean-Baptiste Richard
Caroline Demeret
Johnny Platon
Stéphane Le Vu
Sophie Goyard
Véronique Gillon
Sibylle Bernard-Stoecklin
Bernadette Crescenzo-Chaigne
Gabrielle Jones
Sylvie van der Werf
Olivier Lantz
Thierry Rose
Harold Noël

Résumé

Background: The seroprevalence of SARS-CoV-2 infection in the French population was estimated with a representative, repeated cross-sectional survey based on residual sera from routine blood testing. These data contained no information on infection or vaccination status, thus limiting the ability to detail changes observed in the immunity level of the population over time. Objective: Our aim is to predict the infected or vaccinated status of individuals in the French serosurveillance survey based only on the results of serological assays. Reference data on longitudinal serological profiles of seronegative, infected, and vaccinated individuals from another French cohort were used to build the predictive model. Methods: A model of individual vaccination or infection status with respect to SARS-CoV-2 obtained from a machine learning procedure was proposed based on 3 complementary serological assays. This model was applied to the French nationwide serosurveillance survey from March 2020 to March 2022 to estimate the proportions of the population that were negative, infected, vaccinated, or infected and vaccinated. Results: From February 2021 to March 2022, the estimated percentage of infected and unvaccinated individuals in France increased from 7.5% to 16.8%. During this period, the estimated percentage increased from 3.6% to 45.2% for vaccinated and uninfected individuals and from 2.1% to 29.1% for vaccinated and infected individuals. The decrease in the seronegative population can be largely attributed to vaccination. Conclusions: Combining results from the serosurveillance survey with more complete data from another longitudinal cohort completes the information retrieved from serosurveillance while keeping its protocol simple and easy to implement.
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anses-04395611 , version 1 (15-01-2024)

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Stéphanie Bougeard, Adeline Huneau-Salaun, Mikael Attia, Jean-Baptiste Richard, Caroline Demeret, et al.. Application of Machine Learning Prediction of Individual SARS-CoV-2 Vaccination and Infection Status to the French Serosurveillance Survey From March 2020 to 2022: Cross-Sectional Study. JMIR Public Health and Surveillance, 2023, 9, pp.e46898. ⟨10.2196/46898⟩. ⟨anses-04395611⟩

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