Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests

Abstract

Significance In July 2020, there was great uncertainty around the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite its vital importance for public health policy, knowledge about the cumulative incidence of past infections was limited by challenges with diagnostic testing and the presence of mild or asymptomatic cases. Within this environment, competing narratives emerged around the prevalence of past SARS-CoV-2 infections, which would have had differing policy implications. To address this, in July 2020 a population-representative household survey collected serum for SARS-CoV-2 antibody detection in Ohio in the United States. This study describes a Bayesian statistical method developed to estimate the population prevalence of past infections accounting for the low positive rate; multiple imperfect diagnostic tests; and nonignorable nonresponse. Globally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 59 million people and killed more than 1.39 million. Designing and monitoring interventions to slow and stop the spread of the virus require knowledge of how many people have been and are currently infected, where they live, and how they interact. The first step is an accurate assessment of the population prevalence of past infections. There are very few population-representative prevalence studies of SARS-CoV-2 infections, and only two states in the United States—Indiana and Connecticut—have reported probability-based sample surveys that characterize statewide prevalence of SARS-CoV-2. One of the difficulties is the fact that tests to detect and characterize SARS-CoV-2 coronavirus antibodies are new, are not well characterized, and generally function poorly. During July 2020, a survey representing all adults in the state of Ohio in the United States collected serum samples and information on protective behavior related to SARS-CoV-2 and coronavirus disease 2019 (COVID-19). Several features of the survey make it difficult to estimate past prevalence: 1) a low response rate; 2) a very low number of positive cases; and 3) the fact that multiple poor-quality serological tests were used to detect SARS-CoV-2 antibodies. We describe a Bayesian approach for analyzing the biomarker data that simultaneously addresses these challenges and characterizes the potential effect of selective response. The model does not require survey sample weights; accounts for multiple imperfect antibody test results; and characterizes uncertainty related to the sample survey and the multiple imperfect, potentially correlated tests.