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Dynamics of a Fractional-Order Delayed Model of COVID-19 with Vaccination Efficacy.
Rihan, Fathalla A; Kandasamy, Udhayakumar; Alsakaji, Hebatallah J; Sottocornola, Nicola.
  • Rihan FA; Department of Mathematical Sciences, College of Science, United Arab Emirates University, Al-Ain 15551, United Arab Emirates.
  • Kandasamy U; Department of Mathematical Sciences, College of Science, United Arab Emirates University, Al-Ain 15551, United Arab Emirates.
  • Alsakaji HJ; Department of Mathematical Sciences, College of Science, United Arab Emirates University, Al-Ain 15551, United Arab Emirates.
  • Sottocornola N; College of Natural and Health Sciences, Zayed University, Abu Dhabi P.O. Box 144534, United Arab Emirates.
Vaccines (Basel) ; 11(4)2023 Mar 29.
Article in English | MEDLINE | ID: covidwho-2302549
ABSTRACT
In this study, we provide a fractional-order mathematical model that considers the effect of vaccination on COVID-19 spread dynamics. The model accounts for the latent period of intervention strategies by incorporating a time delay τ. A basic reproduction number, R0, is determined for the model, and prerequisites for endemic equilibrium are discussed. The model's endemic equilibrium point also exhibits local asymptotic stability (under certain conditions), and a Hopf bifurcation condition is established. Different scenarios of vaccination efficacy are simulated. As a result of the vaccination efforts, the number of deaths and those affected have decreased. COVID-19 may not be effectively controlled by vaccination alone. To control infections, several non-pharmacological interventions are necessary. Based on numerical simulations and fitting to real observations, the theoretical results are proven to be effective.
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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 Year: 2023 Document Type: Article Affiliation country: Vaccines11040758

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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 Year: 2023 Document Type: Article Affiliation country: Vaccines11040758