Designing social distancing policies for the COVID-19 pandemic: A probabilistic model predictive control approach

Antonios Armaou; Bryce Katch; Lucia Russo; Constantinos Siettos; Antonios Armaou; Bryce Katch; Lucia Russo; Constantinos Siettos

doi:10.3934/mbe.2022409

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 9: 8804-8832. doi: 10.3934/mbe.2022409

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Designing social distancing policies for the COVID-19 pandemic: A probabilistic model predictive control approach

1.
Dept. of Chemical Engineering, The Pennsylvania State University, USA
2.
Institute of Science and Technology for Energy and Sustainable Mobility, Consiglio Nazionale delle Ricerche, Italy
3.
Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Scuola Superiore Meridionale, Università degli Studi di Napoli Federico Ⅱ, Naples, Italy

Received: 15 December 2021 Revised: 25 January 2022 Accepted: 07 February 2022 Published: 17 June 2022

The effective control of the COVID-19 pandemic is one the most challenging issues of recent years. The design of optimal control policies is challenging due to a variety of social, political, economical and epidemiological factors. Here, based on epidemiological data reported in recent studies for the Italian region of Lombardy, which experienced one of the largest and most devastating outbreaks in Europe during the first wave of the pandemic, we present a probabilistic model predictive control (PMPC) approach for the systematic study of what if scenarios of social distancing in a retrospective analysis for the first wave of the pandemic in Lombardy. The performance of the proposed PMPC was assessed based on simulations of a compartmental model that was developed to quantify the uncertainty in the level of the asymptomatic cases in the population, and the synergistic effect of social distancing during various activities, and public awareness campaign prompting people to adopt cautious behaviors to reduce the risk of disease transmission. The PMPC takes into account the social mixing effect, i.e. the effect of the various activities in the potential transmission of the disease. The proposed approach demonstrates the utility of a PMPC approach in addressing COVID-19 transmission and implementing public relaxation policies.
- COVID-19 pandemic,
- model predictive control,
- forecasting,
- Lombardy Italy,
- social distancing,
- social mixing,
- Google mobility reports
Citation: Antonios Armaou, Bryce Katch, Lucia Russo, Constantinos Siettos. Designing social distancing policies for the COVID-19 pandemic: A probabilistic model predictive control approach[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 8804-8832. doi: 10.3934/mbe.2022409

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Abstract

The effective control of the COVID-19 pandemic is one the most challenging issues of recent years. The design of optimal control policies is challenging due to a variety of social, political, economical and epidemiological factors. Here, based on epidemiological data reported in recent studies for the Italian region of Lombardy, which experienced one of the largest and most devastating outbreaks in Europe during the first wave of the pandemic, we present a probabilistic model predictive control (PMPC) approach for the systematic study of what if scenarios of social distancing in a retrospective analysis for the first wave of the pandemic in Lombardy. The performance of the proposed PMPC was assessed based on simulations of a compartmental model that was developed to quantify the uncertainty in the level of the asymptomatic cases in the population, and the synergistic effect of social distancing during various activities, and public awareness campaign prompting people to adopt cautious behaviors to reduce the risk of disease transmission. The PMPC takes into account the social mixing effect, i.e. the effect of the various activities in the potential transmission of the disease. The proposed approach demonstrates the utility of a PMPC approach in addressing COVID-19 transmission and implementing public relaxation policies.

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