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Prediction of COVID-19 Pandemic Spread in Kingdom of Saudi Arabia

Abdulaziz Attaallah1, Sabita Khatri2, Mohd Nadeem2, Syed Anas Ansar2, Abhishek Kumar Pandey2, Alka Agrawal2,*

1 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Kingdom of Saudi Arabia
2 Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, Uttar Pradesh, India

* Corresponding Author: Alka Agrawal. Email: email

Computer Systems Science and Engineering 2021, 37(3), 313-329.


A significant increase in the number of coronavirus cases can easily be noticed in most of the countries around the world. Inspite of the consistent preventive initiatives being taken to contain the spread of this virus, the unabated increase in the cases is both alarming and intriguing. The role of mathematical models in predicting and estimating the spread of the virus, and identifying various preventive factors dependencies has been found important and effective in most of the previous pandemics like Severe Acute Respiratory Syndrome (SARS) 2003. In this research work, authors have proposed the Susceptible-Infectected-Removed (SIR) model variation in order to forecast the pattern of coronavirus disease (COVID-19) spread for the upcoming eight weeks in perspective of Saudi Arabia. The study has been performed by using SIR model with a proposed simplification using average progression for further estimation of β and γ values for better curve fittings ratios. The predictive results of this study clearly show that under the current public health interventions, there will be an increase in the COVID-19 cases in Saudi Arabia in the next four weeks. Hence, a set of strong health primitives and precautionary measures are recommended in order to avoid and prevent the further spread of COVID-19 in Saudi Arabia.


Cite This Article

A. Attaallah, S. Khatri, M. Nadeem, S. Anas Ansar, A. Kumar Pandey et al., "Prediction of covid-19 pandemic spread in kingdom of saudi arabia," Computer Systems Science and Engineering, vol. 37, no.3, pp. 313–329, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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