FORECASTING THE COVID-19 USING THE DISCRETE GENERALIZED LOGISTIC MODEL

Anis Ben Dhahbi, Yassine Chargui, Salah Boulaaras, Seyfeddine Rahali and Abada Mhamdi
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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

FRACTALS (fractals), 2022, vol. 30, issue 10, 1-10

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.

Keywords: Mathematical Modeling; COVID-19; Generalized Logistic Model (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22402563

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