Title: Multilevel evidence on how policymakers may reduce avoidable deaths due to COVID-19: the case of Brazil

Authors: Rafael De Freitas Souza; Luiz Paulo Fávero; Michel Ferreira Cardia Haddad; Hamilton Luiz Corrêa

Addresses: School of Economics, Business and Accounting, University of São Paulo, Av. Professor Luciano Gualberto, 908 – FEA 1 – Room G121, SP, 05508-900, Brazil ' School of Economics, Business and Accounting, University of São Paulo, Av. Professor Luciano Gualberto, 908 – FEA 1 – Room G121, SP, 05508-900, Brazil ' Department of Land Economy, University of Cambridge, 16-21 Silver St, Cambridge CB3 9EP, UK ' School of Economics, Business and Accounting, University of São Paulo, Av. Professor Luciano Gualberto, 908 – FEA 1 – Room G121, SP, 05508-900, Brazil

Abstract: We propose a hierarchical linear method to investigate the effectiveness of social distancing measures. By considering the COVID-19 data as a two-level structure, we are able to demonstrate a significant reduction of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases as a direct consequence of lockdown measures adopted by some states in Brazil. The multilevel modelling is the most appropriate method for the case of Brazil due to the fact that political battles between federal and State government leaders have led to the adoption of lockdown measures with distinct levels of flexibility adopted by each of its 27 states. During the outbreak of such a novel and highly contagious disease, decisions made by policymakers either prevent or increase the number of avoidable deaths. Thus, our results are also potentially useful to convince policymakers to adopt reasonable policy measures to shorten the COVID-19 pandemic in the biggest Latin American country.

Keywords: COVID-19; emerging countries; lockdown; multilevel modelling; SARS-CoV-2.

DOI: 10.1504/IJMOR.2021.10038813

International Journal of Mathematics in Operational Research, 2022 Vol.21 No.3, pp.321 - 337

Received: 28 Nov 2020
Accepted: 25 Jan 2021

Published online: 30 Mar 2022 *

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