Optimal Control to Limit the Spread of COVID-19 in Italy

TL;DR

This paper uses optimal control theory on a generalized SEIR model to evaluate strategies like social distancing and treatment to reduce COVID-19 spread in Italy, demonstrating potential reductions in cases and deaths.

Contribution

It introduces a novel optimal control framework applied to a generalized SEIR model tailored for COVID-19, incorporating real Italian data for validation.

Findings

Model accurately predicts quarantine and hospitalization trends.

Optimal controls can significantly reduce infections and fatalities.

Strategies like increasing protected individuals are highly effective.

Abstract

We apply optimal control theory to a generalized SEIR-type model. The proposed system has three controls, representing social distancing, preventive means, and treatment measures to combat the spread of the COVID-19 pandemic. We analyze such optimal control problem with respect to real data transmission in Italy. Our results show the appropriateness of the model, in particular with respect to the number of quarantined/hospitalized (confirmed and infected) and recovered individuals. Considering the Pontryagin controls, we show how in a perfect world one could have drastically diminish the number of susceptible, exposed, infected, quarantined/hospitalized, and death individuals, by increasing the population of insusceptible/protected.