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A caputo fractional order epidemic model for evaluating the effectiveness of high-risk quarantine and vaccination strategies on the spread of COVID-19.
Olayiwola, Morufu Oyedunsi; Alaje, Adedapo Ismaila; Olarewaju, Akeem Yunus; Adedokun, Kamilu Adewale.
  • Olayiwola MO; Department of Mathematical Sciences, Osun State University, Osogbo, Nigeria.
  • Alaje AI; Department of Mathematical Sciences, Osun State University, Osogbo, Nigeria.
  • Olarewaju AY; Department of Mathematical Sciences, Osun State University, Osogbo, Nigeria.
  • Adedokun KA; Department of Mathematical Sciences, Osun State University, Osogbo, Nigeria.
Healthc Anal (N Y) ; 3: 100179, 2023 Nov.
Article in English | MEDLINE | ID: covidwho-2301195
ABSTRACT
The recent global Coronavirus disease (COVID-19) threat to the human race requires research on preventing its reemergence without affecting socio-economic factors. This study proposes a fractional-order mathematical model to analyze the impact of high-risk quarantine and vaccination on COVID-19 transmission. The proposed model is used to analyze real-life COVID-19 data to develop and analyze the solutions and their feasibilities. Numerical simulations study the high-risk quarantine and vaccination strategies and show that both strategies effectively reduce the virus prevalence, but their combined application is more effective. We also demonstrate that their effectiveness varies with the volatile rate of change in the system's distribution. The results are analyzed using Caputo fractional order and presented graphically and extensively analyzed to highlight potent ways of curbing the virus.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Language: English Journal: Healthc Anal (N Y) Year: 2023 Document Type: Article Affiliation country: J.health.2023.100179

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Language: English Journal: Healthc Anal (N Y) Year: 2023 Document Type: Article Affiliation country: J.health.2023.100179