Larabi-Marie-Sainte Souad, Alhalawani Sawsan, Shaheen Sara, Almustafa Khaled Mohamad, Saba Tanzila, Khan Fatima Nayer, Rehman Amjad
Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Department of Information Sciences, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Heliyon. 2022 Jun;8(6):e09578. doi: 10.1016/j.heliyon.2022.e09578. Epub 2022 Jun 2.
Many countries are suffering from the COVID19 pandemic. The number of confirmed cases, recovered, and deaths are of concern to the countries having a high number of infected patients. Forecasting these parameters is a crucial way to control the spread of the disease and struggle with the pandemic. This study aimed at forecasting the number of cases and deaths in KSA using time-series and well-known statistical forecasting techniques including Exponential Smoothing and Linear Regression. The study is extended to forecast the number of cases in the main countries such that the US, Spain, and Brazil (having a large number of contamination) to validate the proposed models (Drift, SES, Holt, and ETS). The forecast results were validated using four evaluation measures. The results showed that the proposed ETS (resp. Drift) model is efficient to forecast the number of cases (resp. deaths). The comparison study, using the number of cases in KSA, showed that ETS (with RMSE reaching 18.44) outperforms the state-of-the art studies (with RMSE equal to 107.54). The proposed forecasting model can be used as a benchmark to tackle this pandemic in any country.
许多国家都在遭受新冠疫情的困扰。确诊病例、康复病例和死亡病例的数量是感染患者数量众多的国家所关注的问题。预测这些参数是控制疾病传播和应对疫情的关键途径。本研究旨在使用时间序列以及包括指数平滑法和线性回归在内的著名统计预测技术，预测沙特阿拉伯的病例数和死亡数。该研究进一步扩展到预测主要国家（如美国、西班牙和巴西，这些国家感染情况严重）的病例数，以验证所提出的模型（漂移模型、简单指数平滑法、霍尔特法和误差、趋势和季节性方法）。预测结果使用四种评估指标进行验证。结果表明，所提出的误差、趋势和季节性方法（相应地，漂移模型）在预测病例数（相应地，死亡数）方面是有效的。使用沙特阿拉伯的病例数进行的比较研究表明，误差、趋势和季节性方法（均方根误差达到18.44）优于现有研究（均方根误差等于107.54）。所提出的预测模型可作为任何国家应对这一疫情的基准。
