Analyzing and forecasting COVID‐19 pandemic in the Kingdom of Saudi Arabia using ARIMA and SIR models. (5th October 2020)

• Record Type: Journal Article
• Authors: Abuhasel, Khaled Ali; Khadr, Mosaad; Alquraish, Mohammed M.
• Is Part Of: Computational intelligence. Volume 38:Number 3(2022)
• Page Start: 770
• Page End: 783
• Publication Date: 2020-10-05
• Subjects: ARIMA model -- COVID‐19 -- forecasting -- Saudi Arabia -- SIR model; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12407
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: The novel coronavirus COVID‐19 is spreading all across the globe. By June 29, 2020, the World Health Organization announced that the number of cases worldwide had reached 9 994 206 and resulted in more than 499 024 deaths. The earliest case of COVID‐19 in the Kingdom of Saudi Arabia (KSA) was registered on March 2 in 2020. Since then, the number of infections as per the outcome of the tests increased gradually on a daily basis. The KSA has 182 493 cases, with 124 755 recoveries and 1551 deaths on June 29, 2020. There have been significant efforts to develop models that forecast the risks, parameters, and impacts of this epidemic. These models can aid in controlling and preventing the outbreak of these infections. In this regard, this article details the extent to which the infection cases, prevalence, and recovery rate of this pandemic are in the country and the predictions that can be made using the past and current data. The well‐known classical SIR model was applied to predict the highest number of cases that may be realized and the flattening of the curve afterward. On the other hand, the ARIMA model was used to predict the prevalence cases. Results of the SIR model indicate that the repatriation plan reduced the estimated reproduction number. The results further affirm that the containment technique used by Saudi Arabia to curb the spread of the disease was efficient. Moreover, using the results, close interaction between people, despite the current measures remains a great risk factor to the spread of the disease. This may force the government to take even more stringent measures. By validating the performance of the applied models, ARIMA proved to be a good forecasting method from current data. The past data and the forecasted data, as per the ARIMA model provided high correlation, showing that there were minimum errors.