Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/218732
Authors: 
Sánchez-Romero, Miguel
Di Lego, Vanessa
Fürnkranz-Prskawetz, Alexia
Queiroz, Bernardo Lanza
Year of Publication: 
2020
Series/Report no.: 
ECON WPS No. 05/2020
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 (c) 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.
Document Type: 
Working Paper

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