Combining antibody markers for serosurveillance of SARS-CoV-2 to estimate seroprevalence and time-since-infection (preprint)

ABSTRACT

Serosurveillance is an important epidemiologic tool for SARS-CoV-2, used to estimate burden of disease and degree of population immunity. Which antibody biomarker, and the optimal number of biomarkers, has not been well-established, especially with the emerging rollout of vaccines globally. Here, we used random forest models to demonstrate that a single spike or receptor-binding domain (RBD) antibody was adequate for classifying prior infection, while a combination of two antibody biomarkers performed better than any single marker for estimating time-since-infection. Nucleocapsid antibodies performed worse than spike or RBD antibodies for classification, but is of utility for estimating time-since-infection, and in distinguishing infection-induced from vaccine-induced responses. Our analysis has the potential to inform the design of serosurveys for SARS-CoV-2, including decisions regarding number of antibody biomarkers measured.