Bayesian Estimation of the Seroprevalence of Antibodies to SARS-CoV-2

Dong, Qunfeng; Gao, Xiang

Accurate estimations of the seroprevalence of antibodies to SARS-CoV-2 need to properly consider the specificity and sensitivity of the antibody tests. In addition, prior knowledge of the extent of viral infection in a population may also be important for adjusting the estimation of seroprevalence. For this purpose, we have developed a Bayesian approach that can incorporate the variabilities of specificity and sensitivity of the antibody tests, as well as the prior probability distribution of seroprevalence. We have demonstrated the utility of our approach by applying it to a recently published large-scale dataset from the U.S. CDC, with our results providing entire probability distributions of seroprevalence instead of single point estimates. Our Bayesian code is freely available at https://github.com/qunfengdong/AntibodyTest. Lay summary To estimate the extent of the viral infection, we have developed a statistical method that can incorporate the variabilities of specificity and sensitivity of the antibody tests. Our computer code is freely available at https://github.com/qunfengdong/AntibodyTest.