IMPACTS OF QUANTIFYING SOCIAL DISTANCING MEASURES ON MPC PERFORMANCE FOR SIR-TYPE SYSTEMS

Authors

  • Juan Esteban Sereno Mesa Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina
  • Antonio Ferramosca University of Bergamo
  • Alejandro H. González Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina
  • Agustina D' Jorge Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina

DOI:

https://doi.org/10.52292/j.laar.2023.3278

Keywords:

Epidemiological models, Model predictive control, Non-pharmaceutical interventions, Discrete control actions

Abstract

Currently, there has been a sharp increase in epidemic control research as a result of recent epidemic outbreaks. Several strategies aiming to minimize the Epidemic Final Size and/or to keep the Infected Peak Prevalence under a specific value were proposed.

However, not many strategies focused on analyzing the impact of applying quantified measures instead of continuous control action. This analysis is a crucial aspect since policymakers design their non-pharmaceutical intervention based on a discrete scale of intensity, from mask-wearing to hard lockdown.

In this work, we present a quantized-input non-linear Model Predictive Control strategy to manage non-pharmaceutical interventions during an epidemic outbreak. The impact of quantifying the social distancing measure is computed through several simulations based on a COVID-19 epidemic model and considering different quantization levels of the non-pharmaceutical intervention. Finally, the control performance in each quantization level is evaluated with the computation of four epidemic indices.

Author Biographies

Juan Esteban Sereno Mesa, Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina

Institute of Technological Development for the Chemical Industry (INTEC),

CONICET-UNL,

Santa Fe, Argentina

Antonio Ferramosca, University of Bergamo

Department of Management, Information and Production Engineering, University of Bergamo, Bergamo, Italy

Alejandro H. González, Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina

Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina

Agustina D' Jorge, Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina

Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina

Published

2023-07-18

Issue

Section

28th Congreso de la Asociacion Argentina de Control Automático