Serosurveillance is an important epidemiologic tool for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), used to estimate infection rates and the degree of population immunity. There is no general agreement on which antibody biomarker(s) should be used, especially with the 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 can be useful 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 a number of antibody biomarkers measured.
Keywords: Antibody; COVID-19; SARS-CoV-2; machine learning; seroprevalence; serosurveillance.