Optimal screening strategies in the control of an infectious disease: a case of the COVID-19 in a population with age structure

TL;DR

This paper develops an optimal control model for COVID-19 quarantine strategies in an age-structured population, balancing health outcomes and economic costs to guide policy decisions.

Contribution

It introduces a novel age-structured optimal control framework for quarantine strategies that jointly considers health and economic impacts.

Findings

Optimal quarantine schedules vary with age groups.

Delay in quarantine initiation increases mortality.

Economic costs influence the timing of isolation measures.

Abstract

After the COVID-19 pandemic, we saw an increase in demand for epidemiological mathematical models. The goal of this work is to study the optimal control for an age-structured model as a strategy of quarantine of infected people, which is done via Pontryagin's maximum principle. Since quarantine campaigns are not just a matter of public health, also posing economic challenges, the optimal control problem does not simply minimize the number of infected individuals. Instead, it jointly minimizes this number and the economic costs associated to the campaigns, providing data that can help authorities make decisions when dealing with epidemics. The controls are the quarantine entrance parameters, which are numerically calculated for different lengths of isolation. The best strategies gives a calendar that indicates when the isolation measures can be relaxed, and the consequences of a delay in the start of the quarantine are analyzed by presenting the reduction in the number of deaths for the strategy with optimal control compared to a no-quarantine landscape.