A control framework to optimize public health policies in the course of the COVID-19 pandemic

Abstract The SARS-CoV-2 pandemic triggered substantial economic and social disruptions. Mitigation policies varied across countries based on resources, political conditions, and human behavior. In the absence of widespread vaccination able to induce herd immunity, strategies to coexist with the viru...

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Autores principales:	Igor M. L. Pataro, Juliane F. Oliveira, Marcelo M. Morato, Alan A. S. Amad, Pablo I. P. Ramos, Felipe A. C. Pereira, Mateus S. Silva, Daniel C. P. Jorge, Roberto F. S. Andrade, Mauricio L. Barreto, Marcus Americano da Costa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5c28e7d9239a41a5856a85e2a273fb8e
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spelling	oai:doaj.org-article:5c28e7d9239a41a5856a85e2a273fb8e2021-12-02T16:31:54ZA control framework to optimize public health policies in the course of the COVID-19 pandemic10.1038/s41598-021-92636-82045-2322https://doaj.org/article/5c28e7d9239a41a5856a85e2a273fb8e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92636-8https://doaj.org/toc/2045-2322Abstract The SARS-CoV-2 pandemic triggered substantial economic and social disruptions. Mitigation policies varied across countries based on resources, political conditions, and human behavior. In the absence of widespread vaccination able to induce herd immunity, strategies to coexist with the virus while minimizing risks of surges are paramount, which should work in parallel with reopening societies. To support these strategies, we present a predictive control system coupled with a nonlinear model able to optimize the level of policies to stop epidemic growth. We applied this system to study the unfolding of COVID-19 in Bahia, Brazil, also assessing the effects of varying population compliance. We show the importance of finely tuning the levels of enforced measures to achieve SARS-CoV-2 containment, with periodic interventions emerging as an optimal control strategy in the long-term.Igor M. L. PataroJuliane F. OliveiraMarcelo M. MoratoAlan A. S. AmadPablo I. P. RamosFelipe A. C. PereiraMateus S. SilvaDaniel C. P. JorgeRoberto F. S. AndradeMauricio L. BarretoMarcus Americano da CostaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Igor M. L. Pataro Juliane F. Oliveira Marcelo M. Morato Alan A. S. Amad Pablo I. P. Ramos Felipe A. C. Pereira Mateus S. Silva Daniel C. P. Jorge Roberto F. S. Andrade Mauricio L. Barreto Marcus Americano da Costa A control framework to optimize public health policies in the course of the COVID-19 pandemic
description	Abstract The SARS-CoV-2 pandemic triggered substantial economic and social disruptions. Mitigation policies varied across countries based on resources, political conditions, and human behavior. In the absence of widespread vaccination able to induce herd immunity, strategies to coexist with the virus while minimizing risks of surges are paramount, which should work in parallel with reopening societies. To support these strategies, we present a predictive control system coupled with a nonlinear model able to optimize the level of policies to stop epidemic growth. We applied this system to study the unfolding of COVID-19 in Bahia, Brazil, also assessing the effects of varying population compliance. We show the importance of finely tuning the levels of enforced measures to achieve SARS-CoV-2 containment, with periodic interventions emerging as an optimal control strategy in the long-term.
format	article
author	Igor M. L. Pataro Juliane F. Oliveira Marcelo M. Morato Alan A. S. Amad Pablo I. P. Ramos Felipe A. C. Pereira Mateus S. Silva Daniel C. P. Jorge Roberto F. S. Andrade Mauricio L. Barreto Marcus Americano da Costa
author_facet	Igor M. L. Pataro Juliane F. Oliveira Marcelo M. Morato Alan A. S. Amad Pablo I. P. Ramos Felipe A. C. Pereira Mateus S. Silva Daniel C. P. Jorge Roberto F. S. Andrade Mauricio L. Barreto Marcus Americano da Costa
author_sort	Igor M. L. Pataro
title	A control framework to optimize public health policies in the course of the COVID-19 pandemic
title_short	A control framework to optimize public health policies in the course of the COVID-19 pandemic
title_full	A control framework to optimize public health policies in the course of the COVID-19 pandemic
title_fullStr	A control framework to optimize public health policies in the course of the COVID-19 pandemic
title_full_unstemmed	A control framework to optimize public health policies in the course of the COVID-19 pandemic
title_sort	control framework to optimize public health policies in the course of the covid-19 pandemic
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5c28e7d9239a41a5856a85e2a273fb8e
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A control framework to optimize public health policies in the course of the COVID-19 pandemic

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