Estimating population infection rates from non-random testing data: Evidence from the COVID-19 pandemic.

To effectively respond to an emerging infectious disease outbreak, policymakers need timely and accurate measures of disease prevalence in the general population. This paper presents a new methodology to estimate real-time population infection rates from non-random testing data. The approach compares how the observed positivity rate varies with the size of the tested population and applies this gradient to infer total population infections. Applying this methodology to daily testing data across U.S. states during the first wave of the COVID-19 pandemic, we estimated widespread undiagnosed COVID-19 infections. Nationwide, we found that for every identified case, there were 12 population infections. Our prevalence estimates align with results from seroprevalence surveys, alternate approaches to measuring COVID-19 infections, and total excess mortality during the first wave of the pandemic.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Benatia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)