An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.

ABSTRACT: The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S.

SUBMITTER: Sanchez-Romero M 

PROVIDER: S-EPMC7842943 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature