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Forecasting The Covid-19 Using The Discrete Generalized Logistic Model

Author

Listed:
  • ANIS BEN DHAHBI

    (Department of Physics, College of Science and Arts, at ArRass, Qassim University, Buraidah, Saudi Arabia)

  • YASSINE CHARGUI

    (Department of Physics, College of Science and Arts, at ArRass, Qassim University, Buraidah, Saudi Arabia)

  • SALAH BOULAARAS

    (Department of Mathematics, College of Science and Arts, at ArRass, Qassim University, Buraidah, Saudi Arabia)

  • SEYFEDDINE RAHALI

    (Department of Chemistry, College of Science and Arts, at ArRass, Qassim University, Buraidah, Saudi Arabia)

  • ABADA MHAMDI

    (University of Tunis El Manar, Faculty of Medicine of Tunis, 1006 Tunis, Tunisia)

Abstract

Using mathematical models to describe the dynamics of infectious-diseases transmission in large communities can help epidemiological scientists to understand different factors affecting epidemics as well as health authorities to decide measures effective for infection prevention. In this study, we use a discrete version of the Generalized Logistic Model (GLM) to describe the spread of the coronavirus disease 2019 (COVID-19) pandemic in Saudi Arabia. We assume that we are operating in discrete time so that the model is represented by a first-order difference equation, unlike time-continuous models, which employ differential equations. Using this model, we forecast COVID-19 spread in Saudi Arabia and we show that the short-term predicted number of cumulative cases is in agreement with the confirmed reports.

Suggested Citation

  • Anis Ben Dhahbi & Yassine Chargui & Salah Boulaaras & Seyfeddine Rahali & Abada Mhamdi, 2022. "Forecasting The Covid-19 Using The Discrete Generalized Logistic Model," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(10), pages 1-10, December.
    • Handle: RePEc:wsi:fracta:v:30:y:2022:i:10:n:s0218348x22402563
    DOI: 10.1142/S0218348X22402563
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