A Data-Driven Model Predictive Controller to manage epidemics: The case of SARS-CoV-2 in Mauritius

TL;DR

This paper presents a data-driven model predictive control approach to optimize social isolation policies during epidemics, specifically applied to COVID-19 in Mauritius, to reduce hospitalizations and deaths effectively.

Contribution

It introduces a mixed integer MPC scheme based on an SIHRD model identified from real data, incorporating finite isolation levels and timing constraints for epidemic management.

Findings

Hospitalization stays within health capacity

Significant reduction in deaths with increased isolation levels

Smoother containment with multiple isolation levels

Abstract

This work investigates the benefits of implementing a systematic approach to social isolation policies during epidemics. We develop a mixed integer data-driven model predictive control (MPC) scheme based on an SIHRD model which is identified from available data. The case of the spread of the SARS-CoV-2 virus (also known as COVID-19) in Mauritius is used as a reference point with data obtained during the period December 2021 to May 2022. The isolation scheme is designed with the control decision variable taking a finite set of values corresponding to the desired level of isolation. The control input is further restricted to shifting between levels only after a minimum amount of time. The simulation results validate our design, showing that the need for hospitalisation remains within the capacity of the health centres, with the number of deaths considerably reduced by raising the level of isolation for short periods of time with negligible social and economic impact. We also show that the introduction of additional isolation levels results in a smoother containment approach with a considerably reduced hospitalisation burden.