A Control Framework to Optimize Public Health Policies in the Course of the COVID-19 Pandemic

Overview

Journal Sci Rep

Specialty Science

Date 2021 Jun 29

PMID 34183727

Citations 5

Authors

Igor M L Pataro

Juliane F Oliveira

Marcelo M Morato

Alan A S Amad

Pablo I P Ramos

Felipe A C Pereira

Mateus S Silva

Daniel C P Jorge

Roberto F S Andrade

Mauricio L Barreto

Marcus Americano da Costa

Affiliations

Soon will be listed here.

Abstract

The SARS-CoV-2 pandemic triggered substantial economic and social disruptions. Mitigation policies varied across countries based on resources, political conditions, and human behavior. In the absence of widespread vaccination able to induce herd immunity, strategies to coexist with the virus while minimizing risks of surges are paramount, which should work in parallel with reopening societies. To support these strategies, we present a predictive control system coupled with a nonlinear model able to optimize the level of policies to stop epidemic growth. We applied this system to study the unfolding of COVID-19 in Bahia, Brazil, also assessing the effects of varying population compliance. We show the importance of finely tuning the levels of enforced measures to achieve SARS-CoV-2 containment, with periodic interventions emerging as an optimal control strategy in the long-term.

Citing Articles

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