Estimating SARS-CoV-2 Seroprevalence

Governments and public health authorities use seroprevalence studies to guide their responses to the COVID-19 pandemic. These seroprevalence surveys estimate the proportion of persons within a given population who have detectable antibodies to SARS-CoV-2. However, serologic assays are prone to misclassification error due to false positives and negatives, and non-probability sampling methods may induce selection bias. In this paper, we consider nonparametric and parametric prevalence estimators that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the finite sample performance of the estimators over a range of assay characteristics and sampling scenarios. The methods are used to estimate SARS-CoV-2 seroprevalence in asymptomatic individuals in Belgium and North Carolina.