Nonlinear adaptive control of COVID-19 with media campaigns and treatment

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the infection of severe acute respiratory syndrome coronavirus 2, which is spreading all over the world and causing huge human and economic losses. For these reasons, we study the adaptive control problem of COVID-19 in considera...

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Autores principales: Cao, Boqiang, Kang, Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832491/
https://www.ncbi.nlm.nih.gov/pubmed/33461738
http://dx.doi.org/10.1016/j.bbrc.2020.12.105
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author Cao, Boqiang
Kang, Ting
author_facet Cao, Boqiang
Kang, Ting
author_sort Cao, Boqiang
collection PubMed
description Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the infection of severe acute respiratory syndrome coronavirus 2, which is spreading all over the world and causing huge human and economic losses. For these reasons, we study the adaptive control problem of COVID-19 in consideration of media campaigns and treatment in this paper. Firstly, a novel compartment model is constructed by analysing the spread mechanism of COVID-19 and a nonlinear adaptive control problem is established. Then, using the estimation of parameters updated by adaptive laws, the controllers are designed to achieve the control goals. Finally, numerical examples are presented to illustrate the control capability to the outbreak of COVID-19.
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spelling pubmed-78324912021-01-26 Nonlinear adaptive control of COVID-19 with media campaigns and treatment Cao, Boqiang Kang, Ting Biochem Biophys Res Commun Article Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the infection of severe acute respiratory syndrome coronavirus 2, which is spreading all over the world and causing huge human and economic losses. For these reasons, we study the adaptive control problem of COVID-19 in consideration of media campaigns and treatment in this paper. Firstly, a novel compartment model is constructed by analysing the spread mechanism of COVID-19 and a nonlinear adaptive control problem is established. Then, using the estimation of parameters updated by adaptive laws, the controllers are designed to achieve the control goals. Finally, numerical examples are presented to illustrate the control capability to the outbreak of COVID-19. Elsevier Inc. 2021-05-28 2021-01-09 /pmc/articles/PMC7832491/ /pubmed/33461738 http://dx.doi.org/10.1016/j.bbrc.2020.12.105 Text en © 2021 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Cao, Boqiang
Kang, Ting
Nonlinear adaptive control of COVID-19 with media campaigns and treatment
title Nonlinear adaptive control of COVID-19 with media campaigns and treatment
title_full Nonlinear adaptive control of COVID-19 with media campaigns and treatment
title_fullStr Nonlinear adaptive control of COVID-19 with media campaigns and treatment
title_full_unstemmed Nonlinear adaptive control of COVID-19 with media campaigns and treatment
title_short Nonlinear adaptive control of COVID-19 with media campaigns and treatment
title_sort nonlinear adaptive control of covid-19 with media campaigns and treatment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832491/
https://www.ncbi.nlm.nih.gov/pubmed/33461738
http://dx.doi.org/10.1016/j.bbrc.2020.12.105
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