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Study of Dynamics of a COVID-19 Model for Saudi Arabia with Vaccination Rate, Saturated Treatment Function and Saturated Incidence Rate

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  • Rubayyi T. Alqahtani

    (Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Abdelhamid Ajbar

    (Department of Chemical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

This paper proposes, validates and analyzes the dynamics of the susceptible exposed infectious recovered (SEIR) model for the propagation of COVID-19 in Saudi Arabia, which recorded the largest number of cases in the Arab world. The model incorporates a saturated incidence rate, a constant vaccination rate and a nonlinear treatment function. The rate of treatment is assumed to be proportional to the number of infected persons when this number is low and reaches a fixed value for large number of infected individuals. The expression of the basic reproduction number is derived, and the model basic stability properties are studied. We show that when the basic reproduction number is less than one the model can predict both a Hopf and backward bifurcations. Simulations are also provided to fit the model to COVID-19 data in Saudi Arabia and to study the effects of the parameters of the treatment function and vaccination rate on disease control.

Suggested Citation

  • Rubayyi T. Alqahtani & Abdelhamid Ajbar, 2021. "Study of Dynamics of a COVID-19 Model for Saudi Arabia with Vaccination Rate, Saturated Treatment Function and Saturated Incidence Rate," Mathematics, MDPI, vol. 9(23), pages 1-13, December.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3134-:d:695433
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    References listed on IDEAS

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    1. Sardar, Tridip & Nadim, Sk Shahid & Rana, Sourav & Chattopadhyay, Joydev, 2020. "Assessment of lockdown effect in some states and overall India: A predictive mathematical study on COVID-19 outbreak," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Annas, Suwardi & Isbar Pratama, Muh. & Rifandi, Muh. & Sanusi, Wahidah & Side, Syafruddin, 2020. "Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Kevin Linka & Mathias Peirlinck & Francisco Sahli Costabal & Ellen Kuhl, 2020. "Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 23(11), pages 710-717, August.
    4. Sarkar, Kankan & Khajanchi, Subhas & Nieto, Juan J., 2020. "Modeling and forecasting the COVID-19 pandemic in India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Ndaïrou, Faïçal & Area, Iván & Nieto, Juan J. & Silva, Cristiana J. & Torres, Delfim F.M., 2021. "Fractional model of COVID-19 applied to Galicia, Spain and Portugal," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
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