Data-driven optimized control of the COVID-19 epidemics

Abstract Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to pa...

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Autores principales:	Afroza Shirin, Yen Ting Lin, Francesco Sorrentino
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/47c7c885dcf44850a0551e108774d812
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spelling	oai:doaj.org-article:47c7c885dcf44850a0551e108774d8122021-12-02T16:36:12ZData-driven optimized control of the COVID-19 epidemics10.1038/s41598-021-85496-92045-2322https://doaj.org/article/47c7c885dcf44850a0551e108774d8122021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85496-9https://doaj.org/toc/2045-2322Abstract Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant, specific to each area, which contrasts with the implemented system of reopening ‘in phases’. For all the areas considered, this optimal level corresponds to stricter social distancing than the level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. We also consider the case that the transmissibility increases in time (due e.g. to increasingly colder weather), for which we find that the optimal control solution yields progressively stricter measures of social distancing. We finally compute the optimal control solution for a model modified to incorporate the effects of vaccinations on the population and we see that depending on a number of factors, social distancing measures could be optimally reduced during the period over which vaccines are administered to the population.Afroza ShirinYen Ting LinFrancesco SorrentinoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Afroza Shirin Yen Ting Lin Francesco Sorrentino Data-driven optimized control of the COVID-19 epidemics
description	Abstract Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant, specific to each area, which contrasts with the implemented system of reopening ‘in phases’. For all the areas considered, this optimal level corresponds to stricter social distancing than the level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. We also consider the case that the transmissibility increases in time (due e.g. to increasingly colder weather), for which we find that the optimal control solution yields progressively stricter measures of social distancing. We finally compute the optimal control solution for a model modified to incorporate the effects of vaccinations on the population and we see that depending on a number of factors, social distancing measures could be optimally reduced during the period over which vaccines are administered to the population.
format	article
author	Afroza Shirin Yen Ting Lin Francesco Sorrentino
author_facet	Afroza Shirin Yen Ting Lin Francesco Sorrentino
author_sort	Afroza Shirin
title	Data-driven optimized control of the COVID-19 epidemics
title_short	Data-driven optimized control of the COVID-19 epidemics
title_full	Data-driven optimized control of the COVID-19 epidemics
title_fullStr	Data-driven optimized control of the COVID-19 epidemics
title_full_unstemmed	Data-driven optimized control of the COVID-19 epidemics
title_sort	data-driven optimized control of the covid-19 epidemics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/47c7c885dcf44850a0551e108774d812
work_keys_str_mv	AT afrozashirin datadrivenoptimizedcontrolofthecovid19epidemics AT yentinglin datadrivenoptimizedcontrolofthecovid19epidemics AT francescosorrentino datadrivenoptimizedcontrolofthecovid19epidemics
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Data-driven optimized control of the COVID-19 epidemics

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