Dynamics and control of COVID-19 pandemic with nonlinear incidence rates
Abstract 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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Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:101 |
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| Enthalten in: |
Nonlinear dynamics - 101(2020), 3 vom: 25. Juni, Seite 2013-2026 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rohith, G. [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
Bifurcation analysis |
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| Anmerkungen: |
© Springer Nature B.V. 2020 |
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| doi: |
10.1007/s11071-020-05774-5 |
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| 520 | |a Abstract 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. | ||
| 650 | 4 | |a COVID-19 | |
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| 650 | 4 | |a Nonlinear incidence rate | |
| 650 | 4 | |a Bifurcation analysis | |
| 650 | 4 | |a Sliding mode control | |
| 650 | 4 | |a Model-based control | |
| 700 | 1 | |a Devika, K. B. |0 (orcid)0000-0003-0600-1218 |4 aut | |
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