Scientific Reports (Jun 2021)

A control framework to optimize public health policies in the course of the COVID-19 pandemic

  • 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

DOI
https://doi.org/10.1038/s41598-021-92636-8
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 13

Abstract

Read online

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.