Rahimi Iman, Chen Fang, Gandomi Amir H
Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan, Malaysia.
Data Science Institute, University of Technology Sydney, Ultimo, 2007 NSW Australia.
Neural Comput Appl. 2021 Feb 4:1-11. doi: 10.1007/s00521-020-05626-8.
The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.
新型冠状病毒（COVID-19）已在全球200多个国家传播，截至2020年10月10日，确诊病例超过3600万例。因此，已经发布了几种能够预测全球疫情爆发的机器学习模型。本文对针对COVID-19的最重要的机器学习预测模型进行了综述和简要分析。本研究中的工作分为两个部分。在第一部分中，详细的科学计量分析展示了一种用于文献计量分析的有影响力的工具，该分析是对来自Scopus和Web of Science数据库的COVID-19数据进行的。对于上述分析，涉及了关键词和主题领域，而机器学习预测模型的分类、标准评估以及解决方案方法的比较则在工作的第二部分进行了讨论。结论和讨论作为本研究的最后部分给出。
