A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data

Ahmad B. Hassanat, Sami Mnasri, Mohammed A. Aseeri, Khaled Alhazmi, Omar Cheikhrouhou, Ghada Altarawneh, Malek Alrashidi, Ahmad S. Tarawneh, Khalid S. Almohammadi and Hani Almoamari
Additional contact information
Ahmad B. Hassanat: Community College, University of Tabuk, Tabuk 71491, Saudi Arabia
Sami Mnasri: Community College, University of Tabuk, Tabuk 71491, Saudi Arabia
Mohammed A. Aseeri: Communication and Information Technology Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
Khaled Alhazmi: Communication and Information Technology Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
Omar Cheikhrouhou: College of CIT, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Ghada Altarawneh: Accounting Department, Faculty of Business Administration, University of Tabuk, Tabuk 71491, Saudi Arabia
Malek Alrashidi: Community College, University of Tabuk, Tabuk 71491, Saudi Arabia
Ahmad S. Tarawneh: Department of Algorithms and Their Applications, Eötvös Loránd University, H-1053 Budapest, Hungary
Khalid S. Almohammadi: Community College, University of Tabuk, Tabuk 71491, Saudi Arabia
Hani Almoamari: Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia

Sustainability, 2021, vol. 13, issue 9, 1-22

Abstract: The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics.

Keywords: COVID-19; forecasting; infection rate; Gaussian curve fit; regression; prediction; simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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