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Dynamics and control of COVID-19 pandemic with nonlinear incidence rates

World Health Organization (WHO) has declared COVID-19 a pandemic on March 11, 2020. As of May 23, 2020, according to WHO, there are 213 countries, areas or territories with COVID-19 positive cases. To effectively address this situation, it is imperative to have a clear understanding of the COVID-19...

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Autores principales: Rohith, G., Devika, K. B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315126/
https://www.ncbi.nlm.nih.gov/pubmed/32836807
http://dx.doi.org/10.1007/s11071-020-05774-5
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author Rohith, G.
Devika, K. B.
author_facet Rohith, G.
Devika, K. B.
author_sort Rohith, G.
collection PubMed
description World Health Organization (WHO) has declared COVID-19 a pandemic on March 11, 2020. As of May 23, 2020, according to WHO, there are 213 countries, areas or territories with COVID-19 positive cases. To effectively address this situation, it is imperative to have a clear understanding of the COVID-19 transmission dynamics and to concoct efficient control measures to mitigate/contain the spread. In this work, the COVID-19 dynamics is modelled using susceptible–exposed–infectious–removed model with a nonlinear incidence rate. In order to control the transmission, the coefficient of nonlinear incidence function is adopted as the Governmental control input. To adequately understand the COVID-19 dynamics, bifurcation analysis is performed and the effect of varying reproduction number on the COVID-19 transmission is studied. The inadequacy of an open-loop approach in controlling the disease spread is validated via numerical simulations and a robust closed-loop control methodology using sliding mode control is also presented. The proposed SMC strategy could bring the basic reproduction number closer to 1 from an initial value of 2.5, thus limiting the exposed and infected individuals to a controllable threshold value. The model and the proposed control strategy are then compared with real-time data in order to verify its efficacy.
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spelling pubmed-73151262020-06-25 Dynamics and control of COVID-19 pandemic with nonlinear incidence rates Rohith, G. Devika, K. B. Nonlinear Dyn Original Paper World Health Organization (WHO) has declared COVID-19 a pandemic on March 11, 2020. As of May 23, 2020, according to WHO, there are 213 countries, areas or territories with COVID-19 positive cases. To effectively address this situation, it is imperative to have a clear understanding of the COVID-19 transmission dynamics and to concoct efficient control measures to mitigate/contain the spread. In this work, the COVID-19 dynamics is modelled using susceptible–exposed–infectious–removed model with a nonlinear incidence rate. In order to control the transmission, the coefficient of nonlinear incidence function is adopted as the Governmental control input. To adequately understand the COVID-19 dynamics, bifurcation analysis is performed and the effect of varying reproduction number on the COVID-19 transmission is studied. The inadequacy of an open-loop approach in controlling the disease spread is validated via numerical simulations and a robust closed-loop control methodology using sliding mode control is also presented. The proposed SMC strategy could bring the basic reproduction number closer to 1 from an initial value of 2.5, thus limiting the exposed and infected individuals to a controllable threshold value. The model and the proposed control strategy are then compared with real-time data in order to verify its efficacy. Springer Netherlands 2020-06-25 2020 /pmc/articles/PMC7315126/ /pubmed/32836807 http://dx.doi.org/10.1007/s11071-020-05774-5 Text en © Springer Nature B.V. 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Rohith, G.
Devika, K. B.
Dynamics and control of COVID-19 pandemic with nonlinear incidence rates
title Dynamics and control of COVID-19 pandemic with nonlinear incidence rates
title_full Dynamics and control of COVID-19 pandemic with nonlinear incidence rates
title_fullStr Dynamics and control of COVID-19 pandemic with nonlinear incidence rates
title_full_unstemmed Dynamics and control of COVID-19 pandemic with nonlinear incidence rates
title_short Dynamics and control of COVID-19 pandemic with nonlinear incidence rates
title_sort dynamics and control of covid-19 pandemic with nonlinear incidence rates
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315126/
https://www.ncbi.nlm.nih.gov/pubmed/32836807
http://dx.doi.org/10.1007/s11071-020-05774-5
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