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Fractional-Order SEIRD Model for Global COVID-19 Outbreak

Author

Listed:
  • Rana Yousif

    (Department of Mathematics, Faculty of Sciences of Sfax, University of Sfax, Road of Soukra Km 3.5, B.P. 1171, Sfax 3000, Tunisia
    These authors contributed equally to this work.)

  • Aref Jeribi

    (Department of Mathematics, Faculty of Sciences of Sfax, University of Sfax, Road of Soukra Km 3.5, B.P. 1171, Sfax 3000, Tunisia
    These authors contributed equally to this work.)

  • Saad Al-Azzawi

    (Department of Mathematics, College of Science for Women, Baghdad University, Baghdad 10071, Iraq
    These authors contributed equally to this work.)

Abstract

With the identification of new mutations in the coronavirus with greater transmissibility and pathogenicity, the number of infected people with COVID-19 worldwide has increased as from 22 June 2021, and a new wave has been created. Since the spread of the coronavirus, many studies have been conducted on different groups. The current research was adopted on the implementations of fractional-order (SEIRD: Susceptible, Exposed, Infected, Recovered, Died) people model with a Caputo derivative for investigating the spread of COVID-19. The characteristics of the system, such as the boundedness, existence, uniqueness and non-negativity of the solutions, the equilibrium points of system, and the basic reproduction number, were analyzed. In the numerical part, a simulation for the spread of the virus is presented, which shows that this wave of spread will continue for the next few months and an increasing number of people becoming infected. Furthermore, the numerical results obtained from several types of fractional-order derivatives are compared with real data, which subsequently shows that the Caputo fractional-order derivative follows real data better than others. In addition, the obtained reproduction number has a value greater than one, indicating a continuation of the disease outbreak and the necessity of taking more control decisions.

Suggested Citation

  • Rana Yousif & Aref Jeribi & Saad Al-Azzawi, 2023. "Fractional-Order SEIRD Model for Global COVID-19 Outbreak," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:1036-:d:1072707
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    References listed on IDEAS

    as
    1. Kosmas Kosmidis & Panos Macheras, 2020. "A fractal kinetics SI model can explain the dynamics of COVID-19 epidemics," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-9, August.
    2. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Jagirdar, Mrinal & Lee, Poh Seng, 2018. "Mathematical modeling and performance evaluation of a desiccant coated fin-tube heat exchanger," Applied Energy, Elsevier, vol. 212(C), pages 401-415.
    4. Xu, Haoran & Chen, Bin & Tan, Peng & Cai, Weizi & He, Wei & Farrusseng, David & Ni, Meng, 2018. "Modeling of all porous solid oxide fuel cells," Applied Energy, Elsevier, vol. 219(C), pages 105-113.
    5. Khan, Tahir & Ullah, Zakir & Ali, Nigar & Zaman, Gul, 2019. "Modeling and control of the hepatitis B virus spreading using an epidemic model," Chaos, Solitons & Fractals, Elsevier, vol. 124(C), pages 1-9.
    6. Singh, Jagdev, 2020. "Analysis of fractional blood alcohol model with composite fractional derivative," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Wang, Jin-Liang & Li, Hui-Feng, 2021. "Memory-dependent derivative versus fractional derivative (II): Remodelling diffusion process," Applied Mathematics and Computation, Elsevier, vol. 391(C).
    Full references (including those not matched with items on IDEAS)

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