Obtaining Prevalence Estimates of COVID-19: A Model to Inform Decision-making

Abstract

We evaluate whether randomly sampling and testing a set number of individuals for coronavirus disease 2019 (COVID-19) while adjusting for misclassification error captures the true prevalence. We also quantify the impact of misclassification error bias on publicly reported case data in Maryland. Using a stratified random sampling approach, 50,000 individuals were selected from a simulated Maryland population to estimate the prevalence of COVID-19. We examined the situation when the true prevalence is low (0.07%-2%), medium (2%–5%) and high (6%–10%). Bayesian models informed by published validity estimates were used to account for misclassification error when estimating COVID-19 prevalence. Adjustment for misclassification error captured the true prevalence 100% of the time, irrespective of the true prevalence level. When adjustment for misclassification error was not done, the results highly varied depending on the population’s underlying true prevalence and the type of diagnostic test used. Generally, the prevalence estimates without adjustment for misclassification error worsened as the true prevalence level increased. Adjustment for misclassification error for publicly reported Maryland data led to a minimal but not significant increase in the estimated average daily cases. Random sampling and testing of COVID-19 are needed with adjustment for misclassification error to improve COVID-19 prevalence estimates.