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

Igor M. L. Pataro, Juliane F. Oliveira, Marcelo M. Morato, Alan Amad Orcid Logo, 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

Scientific Reports, Volume: 11, Issue: 1

Swansea University Author: Alan Amad Orcid Logo

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DOI (Published version): 10.1038/s41598-021-92636-8

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 m...

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Published in: Scientific Reports
ISSN: 2045-2322
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa60391
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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.</abstract><type>Journal Article</type><journal>Scientific Reports</journal><volume>11</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2045-2322</issnElectronic><keywords/><publishedDay>28</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-06-28</publishedDate><doi>10.1038/s41598-021-92636-8</doi><url/><notes/><college>COLLEGE NANME</college><department>Electronic and Electrical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>EEEG</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>I.M.L.P. was supported by CNPq (process number 201143/2019-4). 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spelling 2022-08-03T15:40:28.3453387 v2 60391 2022-07-07 A control framework to optimize public health policies in the course of the COVID-19 pandemic fe2123481afa7460a369317354cba4ec 0000-0001-7709-5536 Alan Amad Alan Amad true false 2022-07-07 EEEG 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. Journal Article Scientific Reports 11 1 Springer Science and Business Media LLC 2045-2322 28 6 2021 2021-06-28 10.1038/s41598-021-92636-8 COLLEGE NANME Electronic and Electrical Engineering COLLEGE CODE EEEG Swansea University Another institution paid the OA fee I.M.L.P. was supported by CNPq (process number 201143/2019-4). J.F.O was supported by the Center of Data and Knowledge Integration for Health (CIDACS) through the Zika Platform—a long-term surveillance platform for Zika virus and microcephaly (Unified Health System (SUS), Brazilian Ministry of Health. CIDACS is recipient of a Biomedical Resource Grant from Wellcome Trust, UK). A.A.S.A. gratefully acknowledges the financial support received from the Engineering and Physical Sciences Research Council (EPSRC) in the form of grant EP/R002134/1. R.F.S.A. was supported by the National Institute of Science and Technology—Complex Systems from CNPq, Brazil. M.S.S. was supported by CNPq (process number 117790/2020-6). D.C.J. acknowledges a Scientific Initiation scholarship from CNPq (process number 117568/2019-8). 2022-08-03T15:40:28.3453387 2022-07-07T11:06:36.2519926 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Igor M. L. Pataro 1 Juliane F. Oliveira 2 Marcelo M. Morato 3 Alan Amad 0000-0001-7709-5536 4 Pablo I. P. Ramos 5 Felipe A. C. Pereira 6 Mateus S. Silva 7 Daniel C. P. Jorge 8 Roberto F. S. Andrade 9 Mauricio L. Barreto 10 Marcus Americano da Costa 11 60391__24470__edbf1ec412e3459fbfb05390647f0bac.pdf 60391.VOR.pdf 2022-07-07T11:10:24.0800727 Output 2208873 application/pdf Version of Record true This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. true eng http://creativecommons.org/licenses/by/4.0/
title A control framework to optimize public health policies in the course of the COVID-19 pandemic
spellingShingle A control framework to optimize public health policies in the course of the COVID-19 pandemic
Alan Amad
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 A control framework to optimize public health policies in the course of the COVID-19 pandemic
author_id_str_mv fe2123481afa7460a369317354cba4ec
author_id_fullname_str_mv fe2123481afa7460a369317354cba4ec_***_Alan Amad
author Alan Amad
author2 Igor M. L. Pataro
Juliane F. Oliveira
Marcelo M. Morato
Alan 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
format Journal article
container_title Scientific Reports
container_volume 11
container_issue 1
publishDate 2021
institution Swansea University
issn 2045-2322
doi_str_mv 10.1038/s41598-021-92636-8
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
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description 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.
published_date 2021-06-28T04:18:27Z
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