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

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.

Automated summary

The study discusses the importance of coexisting with the virus and minimizing the risk of epidemic outbreaks in the absence of widespread vaccination. By utilizing a predictive control system and nonlinear model, policies can be optimized to prevent epidemic growth. Fine-tuning enforcement measures and periodic interventions are crucial for achieving SARS-CoV-2 containment in the long term.