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 COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic.

Comparing Waves of COVID-19 in the US: Scale of response changes over time

“…This finding does not contradict the repeated finding of the enormous effect that race, socioeconomic status, and other demographic identities has on COVID-19 infection rates and outcomes [25]. Rather the county level may not be most appropriate to identify county demographic drivers of patterns of pandemic response and instead high quality finer scale data is required to understand inequities in COVID outcomes [26]. We conclude that the response between the 1st and 2nd waves at the county level was largely determined by local feasibility, acceptance, and enforcement of NPIs while the response between waves 2 and 3 was characterized by behavior based on state and regional level guidance, policies, and reporting.…”

Social capital's impact on COVID-19 outcomes at local levels

“…(Other ideal measures, such as excess deaths rates, are not currently available below the county level). Test positivity rates are a useful proxy for the spread of COVID-19, because these rates closely correlate with COVID-19 case rates, rates of residents with COVID-19 antibodies 77 , hospital admissions rates 75 , and death rates 76 . Rates of positive tests have been used to measure spread in studies in US counties 102 , Louisiana county subdivisions 87 , and New York City zipcodes 78 , 79 , among others.…”

Forecasting the COVID-19 Pandemic: Lessons learned and future directions

“…The COVID-19 tracking project’s state-level testing data also served as a useful feature for forecasting deaths. The test positivity rate in a specific state was a good indicator of the severity of the pandemic at that time [4]. The county-level socioeconomic dataset was the only dataset used that was not in a time-series format.…”