Kamalov Firuz, Rajab Khairan, Cherukuri Aswani Kumar, Elnagar Ashraf, Safaraliev Murodbek
Canadian University Dubai, United Arab Emirates.
Najran University, Saudi Arabia.
Neurocomputing (Amst). 2022 Oct 28;511:142-154. doi: 10.1016/j.neucom.2022.09.005. Epub 2022 Sep 8.
The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning.
新冠疫情促使科学家们应用机器学习方法来帮助应对这场危机。尽管已有大量研究，但尚无专门针对研究用于新冠疫情预测的深度学习方法的全面综述。在本文中，我们通过回顾和分析当前使用深度学习进行新冠疫情预测的研究来填补这一文献空白。在我们的综述中，考虑了2020年4月1日至2022年2月20日期间通过谷歌学术搜索到的所有已发表论文和预印本，这些文献描述了用于预测新冠疫情的深度学习方法。我们的搜索共识别出152项研究，其中53项通过了初步质量筛选并被纳入我们的综述。我们提出了一种基于模型的分类法来对文献进行分类。我们描述了每个模型并突出其性能。最后，我们指出了现有方法的不足之处，并阐明了未来研究所需的改进方向。该研究为有兴趣使用深度学习预测新冠疫情的研究人员提供了一个切入点。
