Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States.

ABSTRACT: Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed

SUBMITTER: Chiu WA 

PROVIDER: S-EPMC8448371 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature