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An Extended Fractional SEIR Model to Predict the Spreading Behavior of COVID-19 Disease using Monte Carlo Back Sampling

In: Mathematical Modeling and Intelligent Control for Combating Pandemics

Author

Listed:
  • A. S. Khoojine

    (Faculty of economic and business administration, Yibin University)

  • M. Shadabfar

    (Department of Civil Engineering, Sharif University of Technology)

  • H. Jafari

    (University of South Africa)

  • V. R. Hosseini

    (Institute for Advanced Study, Nanchang University)

Abstract

It is possible to obtain insight into recovery, the rate of moralities, and the spread of diseases, as well as transmission by mathematical modeling. This chapter discusses a dynamic system for the estimation of COVID-19 spread profile, with regard to factors such as social distancing and vaccination. An extended SEIR model is constructed, and its unknown parameters are estimated using the Monte Carlo back analysis technique. Actual infected data are employed for calibrating the model and various transmission attributes of the disease. Moreover, the fractional order of the system of differential equations is considered to evaluate the fractional nature of the spread of COVID-19. The results demonstrate that the model can be used to accurately predict the spread of the disease.

Suggested Citation

  • A. S. Khoojine & M. Shadabfar & H. Jafari & V. R. Hosseini, 2023. "An Extended Fractional SEIR Model to Predict the Spreading Behavior of COVID-19 Disease using Monte Carlo Back Sampling," Springer Optimization and Its Applications, in: Zakia Hammouch & Mohamed Lahby & Dumitru Baleanu (ed.), Mathematical Modeling and Intelligent Control for Combating Pandemics, pages 3-20, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-33183-1_1
    DOI: 10.1007/978-3-031-33183-1_1
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