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Mathematical Modelling and Optimal Control Strategies of a Multistrain COVID-19 Spread
In this paper, we propose a continuous mathematical model that describes the spread of multistrains COVID-19 virus among humans: susceptible, exposed, infected, quarantined, hospitalized, and recovered individuals. The positivity and boundedness of the system solution are provided in order to get th...
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Published in: | Journal of applied mathematics 2022-07, Vol.2022, p.1-14 |
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description | In this paper, we propose a continuous mathematical model that describes the spread of multistrains COVID-19 virus among humans: susceptible, exposed, infected, quarantined, hospitalized, and recovered individuals. The positivity and boundedness of the system solution are provided in order to get the well posedness of the proposed model. Secondly, three controls are considered in our model to minimize the multistrain spread of the disease, namely, vaccination, security campaigns, social distancing measures, and diagnosis. Furthermore, the optimal control problem and related optimality conditions of the Pontryagin type are discussed with the objective to minimize the number of infected individuals. Finally, numerical simulations are performed in the case of two strains of COVID-19 and with four control strategies. By using the incremental cost-effectiveness ratio (ICER) method, we show that combining vaccination with diagnosis provides the most cost-effective strategy. |
doi_str_mv | 10.1155/2022/9071890 |
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The positivity and boundedness of the system solution are provided in order to get the well posedness of the proposed model. Secondly, three controls are considered in our model to minimize the multistrain spread of the disease, namely, vaccination, security campaigns, social distancing measures, and diagnosis. Furthermore, the optimal control problem and related optimality conditions of the Pontryagin type are discussed with the objective to minimize the number of infected individuals. Finally, numerical simulations are performed in the case of two strains of COVID-19 and with four control strategies. By using the incremental cost-effectiveness ratio (ICER) method, we show that combining vaccination with diagnosis provides the most cost-effective strategy.</description><identifier>ISSN: 1110-757X</identifier><identifier>EISSN: 1687-0042</identifier><identifier>DOI: 10.1155/2022/9071890</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Analysis ; Coronaviruses ; COVID-19 ; Diagnosis ; Disease control ; Disease prevention ; Disease transmission ; Epidemics ; Hospitalization ; Infections ; Investigations ; Mathematical analysis ; Mathematical models ; Mutation ; Numerical analysis ; Optimal control ; Optimization ; Pandemics ; Population ; Quarantine ; Severe acute respiratory syndrome coronavirus 2 ; Surveillance ; Viral diseases ; Viruses</subject><ispartof>Journal of applied mathematics, 2022-07, Vol.2022, p.1-14</ispartof><rights>Copyright © 2022 Bouchaib Khajji et al.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Bouchaib Khajji et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c400t-9710492d7b3c5dc4ddcff7f6afbc0b3fc40e755b84b803be97e9531f646101f23</citedby><cites>FETCH-LOGICAL-c400t-9710492d7b3c5dc4ddcff7f6afbc0b3fc40e755b84b803be97e9531f646101f23</cites><orcidid>0000-0002-0716-4492 ; 0000-0001-7758-2844</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2693593293/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2693593293?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,38516,43895,44590,74284,74998</link.rule.ids></links><search><contributor>Werner, Frank</contributor><contributor>Frank Werner</contributor><creatorcontrib>Khajji, Bouchaib</creatorcontrib><creatorcontrib>Boujallal, Lahoucine</creatorcontrib><creatorcontrib>Balatif, Omar</creatorcontrib><creatorcontrib>Rachik, Mostafa</creatorcontrib><title>Mathematical Modelling and Optimal Control Strategies of a Multistrain COVID-19 Spread</title><title>Journal of applied mathematics</title><description>In this paper, we propose a continuous mathematical model that describes the spread of multistrains COVID-19 virus among humans: susceptible, exposed, infected, quarantined, hospitalized, and recovered individuals. 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subjects | Analysis Coronaviruses COVID-19 Diagnosis Disease control Disease prevention Disease transmission Epidemics Hospitalization Infections Investigations Mathematical analysis Mathematical models Mutation Numerical analysis Optimal control Optimization Pandemics Population Quarantine Severe acute respiratory syndrome coronavirus 2 Surveillance Viral diseases Viruses |
title | Mathematical Modelling and Optimal Control Strategies of a Multistrain COVID-19 Spread |
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