PDF(Pubmed)
Abstract：
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.
摘要：
许多国家正在遭受COVID19大流行。确诊病例数,恢复,和死亡是受感染患者人数众多的国家关注的问题。预测这些参数是控制疾病传播和与大流行作斗争的重要途径。这项研究旨在使用时间序列和包括指数平滑和线性回归在内的众所周知的统计预测技术来预测KSA的病例数和死亡人数。该研究扩展到预测主要国家的病例数量，如美国，西班牙,和巴西(有大量污染)来验证所提出的模型(漂移，SES,霍尔特,和ETS）。采用4种评价方法对预测结果进行了验证。结果表明，拟议的ETS(分别为漂移)模型对预测案例数量(分别为死亡)。比较研究，使用KSA的案件数量，表明ETS（RMSE达到18.44）优于最先进的研究（RMSE等于107.54）。拟议的预测模型可以用作任何国家应对这一流行病的基准。
