Abu-Abdoun Dana I, Al-Shihabi Sameh
Industrial Engineering and Engineering Management Department, University of Sharjah, PO Box Sharjah, 27272, United Arab Emirates.
Industrial Engineering Department, School of Engineering, The University of Jordan, Amman 11942, Jordan.
Intell Syst Appl. 2022 Sep;15:200093. doi: 10.1016/j.iswa.2022.200093. Epub 2022 Jun 18.
The prediction of new COVID-19 cases is crucial for decision makers in many countries. Researchers are continually proposing new models to forecast the future tendencies of this pandemic, among which long short-term memory (LSTM) artificial neural networks have exhibited relative superiority compared to other forecasting techniques. Moreover, the correlation between the spread of COVID-19 and exogenous factors, specifically weather features, has been explored to improve forecasting models. However, contradictory results have been reported regarding the incorporation of weather features into COVID-19 forecasting models. Therefore, this study compares uni-variate with bi- and multi-variate LSTM forecasting models for predicting COVID-19 cases, among which the latter models consider weather features. LSTM models were used to forecast COVID-19 cases in the six Gulf Cooperation Council countries. The root mean square error (RMSE) and coefficient of determination ( ) were employed to measure the accuracy of the LSTM forecasting models. Despite similar weather conditions, the weather features that exhibited the strongest correlation with COVID-19 cases differed among the six countries. Moreover, according to the statistical comparisons that were conducted, the improvements gained by including weather features were insignificant in terms of the RMSE values and marginally significant in terms of the values. Consequently, it is concluded that the uni-variate LSTM models were as good as the best bi- and multi-variate LSTM models; therefore, weather features need not be included. Furthermore, we could not identify a single weather feature that can consistently improve the forecasting accuracy.
对许多国家的决策者而言，预测新型冠状病毒肺炎（COVID-19）新增病例至关重要。研究人员不断提出新模型来预测这一疫情的未来趋势，其中长短期记忆（LSTM）人工神经网络相较于其他预测技术已展现出相对优势。此外，人们还探索了COVID-19传播与外部因素（特别是天气特征）之间的相关性，以改进预测模型。然而，关于将天气特征纳入COVID-19预测模型的研究结果却相互矛盾。因此，本研究比较了用于预测COVID-19病例的单变量与双变量及多变量LSTM预测模型，其中后两种模型考虑了天气特征。利用LSTM模型对海湾阿拉伯国家合作委员会的六个国家的COVID-19病例进行预测。采用均方根误差（RMSE）和决定系数（ ）来衡量LSTM预测模型的准确性。尽管天气条件相似，但与COVID-19病例相关性最强的天气特征在这六个国家中各不相同。此外，根据所进行的统计比较，纳入天气特征在RMSE值方面的改善并不显著，在 值方面仅略有显著。因此，得出的结论是，单变量LSTM模型与最佳的双变量及多变量LSTM模型效果相当；因此，无需纳入天气特征。此外，我们无法确定单一的天气特征能够持续提高预测准确性。
