COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model’s parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities.

中文翻译：

---

基于深度学习的沙特阿拉伯 COVID-19 爆发预测模型

COVID-19 疫情已成为影响 200 多个国家的全球大流行病。预测此次疫情的流行病学行为对于防止其传播具有至关重要的作用。在这项研究中，提出了长短期记忆 (LSTM) 网络作为一种强大的深度学习模型来预测沙特阿拉伯的确诊病例总数、康复病例总数和死亡总数。该模型是使用官方报告的数据进行训练的。确定了模型参数的最佳值，使预测精度最大化。使用七个统计评估标准评估模型的预测精度，即均方根误差（RMSE）、决定系数（R2）、平均绝对误差（MAE）、效率系数（EC）、总体指数（OI）、变异系数（COV），和残余质量系数 (CRM)。获得了合理的预测精度。将建议模型的预测精度与其他两个模型进行比较。第一个是基于统计的模型，称为自回归综合移动平均 (ARIMA)。第二种是基于人工智能的模型，称为非线性自回归人工神经网络 (NARANN)。最后，将提出的 LSTM 模型应用于预测六个不同国家的确诊病例和死亡总数；巴西、印度、沙特阿拉伯、南非、西班牙和美国。这些国家由于政策不同，年龄结构、天气和文化不同，流行趋势也不同。假设在预测期内保持不同国家采用的社会距离和保护措施。