Machine learning prediction of individual vaccination and infection status with respect to SARS-CoV-2. Application to the French serosurveillance survey from March 2020 to March 2022. (Preprint)

BACKGROUND The seroprevalence of Severe Acute Respiratory Syndrome Coronavirus 2 (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 had no information on infection or vaccination status, therefore limiting the ability to detail evolutions observed in the immunity level of the population over time. OBJECTIVE Our aim is to predict the infected or vaccinated status of subjects in the French serosurveillance survey only based on results of serological assays. Reference data on longitudinal serological profiles of seronegative, infected and vaccinated subjects 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 three 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 subjects 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 non-infected subjects, and from 2.1% to 29.1% for vaccinated and infected subjects. The major part of the decrease of the seronegative population can be 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.