How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates
Sanchez-Romero, M.; di Lego, V.; Prskawetz, A.; L Queiroz, B.
Show abstract
In this work, we assess the global impact of COVID-19 showing how demographic factors, testing policies and herd immunity are key for saving lives. We extend a standard epidemiological SEIR model in order to: (a) identify the role of demographics (population size and population age distribution) on COVID-19 fatality rates; (b) quantify the maximum number of lives that can be saved according to different testing strategies, different levels of herd immunity, and specific population characteristics; and (d) infer from the observed case fatality rates (CFR) what the true fatality rate might be. Different from previous SEIR model extensions, we implement a Bayesian Melding method in our calibration strategy which enables us to account for data limitation on the total number of deaths. We derive a distribution of the set of parameters that best replicate the observed evolution of deaths by using information from both the model and the data. One Sentence SummaryDemographics factors, testing policies and herd immunity are key for quantifying the maximum number of lives that can be saved from COVID-19.
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