Ghany Kareem Kamal A, Zawbaa Hossam M, Sabri Heba M
College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia.
Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef, Egypt.
Inform Med Unlocked. 2021;23:100566. doi: 10.1016/j.imu.2021.100566. Epub 2021 Apr 6.
Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE), Kuwait, Bahrain, Oman, and Qatar. For this aim, we accessed the time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). The timeline of our data is from January 22, 2020 to January 25, 2021. We have implemented the proposed model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) to predict COVID-19 confirmed and death cases. From the experimental results, we confirmed that KSA and Qatar would take the most extended period to recover from the COVID-19 virus, and the situation will be controllable in the second half of March 2021 in UAE, Kuwait, Oman, and Bahrain. Also, we calculated the root mean square error (RMSE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and death cases are 320.79 and 1.84, respectively, and both are related to Bahrain. While the worst values are 1768.35 and 21.78, respectively, and both are related to KSA. On the other hand, we also calculated the mean absolute relative errors (MARE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and deaths cases are 37.76 and 0.30, and these are related to Kuwait and Qatar respectively. While the worst values are 71.45 and 1.33, respectively, and both are related to KSA.
新型冠状病毒肺炎（COVID-19）是2020年的黑天鹅事件。尽管如此，人类抑制该病毒的应对措施也在不同系统中引发了巨大波动，如卫生、经济、教育和旅游业等。本文着重研究并应用人工智能（AI）算法，利用现有的时间序列数据预测COVID-19的传播情况，并研究生活质量、检测数量以及海湾地区海湾合作委员会（GCC）国家公民对该病毒的认知程度所产生的影响。因此，我们聚焦于沙特阿拉伯王国（KSA）、阿拉伯联合酋长国（UAE）、科威特、巴林、阿曼和卡塔尔的病例情况。为此，我们获取了从约翰·霍普金斯大学系统科学与工程中心（JHU CSSE）收集的时间序列真实数据集。我们的数据时间跨度为2020年1月22日至2021年1月25日。我们基于具有十个隐藏单元（神经元）的长短期记忆（LSTM）实现了所提出的模型，以预测COVID-19确诊病例和死亡病例。从实验结果来看，我们证实沙特阿拉伯和卡塔尔从COVID-19病毒中恢复所需的时间最长，而在阿联酋（UAE）、科威特、阿曼和巴林，情况将在2021年3月下旬得到控制。此外，我们计算了每个国家确诊病例和死亡病例实际值与预测值之间的均方根误差（RMSE），发现确诊病例和死亡病例的最佳值分别为320.79和1.84，且均与巴林相关。而最差值分别为1768.35和21.78，且均与沙特阿拉伯相关。另一方面，我们还计算了每个国家确诊病例和死亡病例实际值与预测值之间的平均绝对相对误差（MARE），发现确诊病例和死亡病例的最佳值分别为37.76和0.30，分别与科威特和卡塔尔相关。而最差值分别为71.45和1.33，且均与沙特阿拉伯相关。
