Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia

Abstract

The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the spread of new COVID-19 cases is critical to develop a framework for universal pandemic preparedness as well as mitigating the disease’s spread. To this end, the main aim of this paper is first to analyze the historical data of the disease gathered from 2 March 2020 to 20 June 2022 and second to use the collected data for forecasting the trajectory of COVID-19 in order to construct robust and accurate models. To the best of our knowledge, this study is the first that analyzes the outbreak of COVID-19 in Saudi Arabia for a long period (more than two years). To achieve this study aim, two techniques from the data analytics field, namely the auto-regressive integrated moving average (ARIMA) statistical technique and Prophet Facebook machine learning technique were investigated for predicting daily new infections, recoveries and deaths. Based on forecasting performance metrics, both models were found to be accurate and robust in forecasting the time series of COVID-19 in Saudi Arabia for the considered period (the coefficient of determination for example was in all cases more than 0.96) with a small superiority of the ARIMA model in terms of the forecasting ability and of Prophet in terms of simplicity and a few hyper-parameters. The findings of this study have yielded a realistic picture of the disease direction and provide useful insights for decision makers so as to be prepared for the future evolution of the pandemic. In addition, the results of this study have shown positive healthcare implications of the Saudi experience in fighting the disease and the relative efficiency of the taken measures.

Using fuzzy time series forecasting and Gaussian mixture model to classify and predict new cases of COVID-19 in Saudi Arabia

“…The model parameters were adapted using an inverse gradient ratio approach. It has been established that the proposed model outperforms Type 1 fuzzy logic [10], the system, and the ANN in terms of root mean square error, mean absolute error in percent and mean absolute error. The entire world has been confronted with an unprecedented human challenge as a result of COVID-19, which is caused by a novel coronavirus discovered in 2019, (SARS-CoV-2) [11].…”

The Pattern of COVID-19 Patients at King Salman Specialist Hospital, Hail Region, Saudi Arabia

“…2 is a time series chart of the COVID-19 positivity rate at King Salman Specialist Hospital. The first case of COVID-19 infection in the Kingdom of Saudi Arabia was reported on March 2, 2020 [7]. Since then, the number of infections increased rapidly to reach 788,292 positive cases by mid-June 2022.…”

Comparison of exponential smoothing and ARIMA time series models for forecasting COVID-19 cases: a secondary data analysis

“…Our study findings were in relevant with a study conducted in Saudi Arabia that used the Prophet and ARIMA models for forecasting daily cases, recovery cases, and fatalities for COVID-19. 27 They claimed that neither model was well suited to deal with such data trends. They occasionally performed worse than the baseline model in terms of RMSE and MAE.…”