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How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates

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  • Sánchez-Romero, Miguel
  • Di Lego, Vanessa
  • Fürnkranz-Prskawetz, Alexia
  • Queiroz, Bernardo Lanza

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.

Suggested Citation

  • Sánchez-Romero, Miguel & Di Lego, Vanessa & Fürnkranz-Prskawetz, Alexia & Queiroz, Bernardo Lanza, 2020. "How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates," ECON WPS - Working Papers in Economic Theory and Policy 05/2020, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.
  • Handle: RePEc:zbw:tuweco:052020
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    References listed on IDEAS

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    1. Flávio Codeço Coelho & Cláudia Torres Codeço & M Gabriela M Gomes, 2011. "A Bayesian Framework for Parameter Estimation in Dynamical Models," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-6, May.
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    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Herd immunity

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