An indirect method to monitor the fraction of people ever infected with COVID-19: an application to the United States
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). Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak. NOTE: Although the paper published focuses on the US, the model can be used for any country and hence we have also applied it to Austria.
Team: Vanessa Di Lego, Miguel Sanchez-Romero, Alexia Prskawetz
Research Group: Economic Demography, Health & Longevity
Cooperation Partner: Universidade Federal de Minas Gerais, Cedeplar, Brazil - Bernardo L Queiroz
Time Frame: August 2020 - May 2021