A Scenario-Based Model Predictive Control Scheme for Pandemic Response Through Non-Pharmaceutical Interventions
Herceg, Domagoj Dell'Oro, Marco Bertollo, Riccardo Miura, Fuminari de Klaver, Paul Breschi, Breschi Krishnamoorthy, Dinesh Salazar, Mauro
Herceg, Domagoj
Dell'Oro, Marco
Bertollo, Riccardo
Miura, Fuminari
de Klaver, Paul
Breschi, Breschi
Krishnamoorthy, Dinesh
Salazar, Mauro
Series / Report no.
Open Access
Type
Article
Language
en
Date of publication
2025-09-11
Year of publication
Research Projects
Organizational Units
Journal Issue
Title
A Scenario-Based Model Predictive Control Scheme for Pandemic Response Through Non-Pharmaceutical Interventions
Translated Title
Published in
2025 IEEE Conference on Control Technology and Applications (CCTA), San Diego 2025;139-144
Abstract
This paper presents a scenario-based model predictive control (MPC) scheme designed to control an evolving pandemic via non-pharmaceutical intervention (NPIs). The proposed approach combines predictions of possible pandemic evolution to decide on a level of severity of NPIs to be implemented over multiple weeks to maintain hospital pressure below a prescribed threshold, while minimizing their impact on society. Specifically, we first introduce a compartmental model which divides the population into Susceptible, Infected, Detected, Threatened, Healed, and Expired (SIDTHE) subpopulations and describe its positive invariant set. This model is expressive enough to explicitly capture the fraction of hospitalized individuals while preserving parameter identifiability w.r.t. publicly available datasets. Second, we devise a scenario-based MPC scheme with recourse actions that captures potential uncertainty of the model parameters. e.g., due to population behavior or seasonality. Our results show that the scenariobased nature of the proposed controller manages to adequately respond to all scenarios, keeping the hospital pressure at bay also in very challenging situations when conventional MPC methods fail.