The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes. In this paper, we will outline the main Deep Learning approaches aimed at predicting the spreading of a disease in space and time. The aim is to show the emerging trends in this area of research and provide a general perspective on the possible strategies to approach this problem. In doing so, we will mainly focus on two macro-categories: classical Deep Learning approaches and Hybrid models. Finally, we will discuss the main advantages and disadvantages of different models, and underline the most promising development directions to improve these approaches.
翻译:冠状病毒大流行的出现激发了人们对能够预测病毒传播的预测模型的兴趣,特别是为了促进和支持决策进程。在本文件中,我们将概述旨在预测疾病在空间和时间的传播的主要深学习方法。目的是显示这一研究领域的新趋势,并对解决这一问题的可能战略提供总体看法。在这样做时,我们将主要侧重于两个宏观类别:传统的深层次学习方法和混合模型。最后,我们将讨论不同模型的主要利弊,并强调改进这些方法的最有希望的发展方向。