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

Autorinnen und Autoren Miguel Sanchez-Romero, Vanessa di Lego, Alexia Prskawetz, Bernardo L Queiroz
Institution(en) ÖAW
Zeitpunkt(e)/Zeitraum der Erhebung(en)
Art der Stichprobe und Benennung der Personengruppe(n) und Anzahl
Fragestellung(en) der Befragung

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). 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. (ÖAW: VID)



Diese Information wurde am 16. April 2021 aus dem Repositorium IHS entnommen.

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corona, sleep deprivation, sleep quality, daytime sleepiness, sleep duration, dream, nightmare

Theoretischer Rahmen/Zugang Gesundheit, Psychologie
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