Yu Zehua, Zheng Xianwei, Yang Zhulun, Lu Bowen, Li Xutao, Fu Maxian
College of EngineeringShantou University Shantou Guangdong 515063 China.
School of Mathematics and Big DataFoshan University Foshan Guangdong 528000 China.
IEEE Open J Eng Med Biol. 2021 Mar 4;2:97-103. doi: 10.1109/OJEMB.2021.3063890. eCollection 2021.
The Covid-19 pandemic is still spreading around the world and seriously imperils humankind's health. This swift spread has caused the public to panic and look to scientists for answers. Fortunately, these scientists already have a wealth of data-the Covid-19 reports that each country releases, reports with valuable spatial-temporal properties. These data point toward some key actions that humans can take in their fight against Covid-19. Technically, the Covid-19 records can be described as sequences, which represent spatial-temporal linkages among the data elements with graph structure. Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data. Specifically, IT-GCN introduces ARIMA into GCN to model the data which originate on nodes in a graph, indicating the severity of the pandemic in different cities. Instead of regular spatial topology, we construct the graph nodes with the vectors via ARIMA parameterization to find out the interaction topology underlying in the pandemic data. Experimental results show that IT-GCN is able to capture the comprehensive interaction-temporal topology and achieve well-performed short-term prediction of the Covid-19 daily infected cases in the United States. Our framework outperforms state-of-art baselines in terms of MAE, RMSE and MAPE. We believe that IT-GCN is a valid and reasonable method to forecast the Covid-19 daily infected cases and other related time-series. Moreover, the prediction can assist in improving containment policies.
新冠疫情仍在全球蔓延，严重威胁着人类健康。这种迅速传播引发了公众的恐慌，并促使人们向科学家寻求答案。幸运的是，这些科学家已经掌握了大量数据——各国发布的新冠疫情报告，这些报告具有宝贵的时空属性。这些数据指向了人类在抗击新冠疫情中可以采取的一些关键行动。从技术上讲，新冠疫情记录可以描述为序列，它以图结构表示数据元素之间的时空联系。因此，我们提出了一种新颖的框架——交互-时间图卷积网络（IT-GCN），用于分析疫情数据。具体而言，IT-GCN将自回归积分滑动平均模型（ARIMA）引入图卷积网络（GCN），以对源自图中节点的数据进行建模，这些数据表明了不同城市疫情的严重程度。我们通过ARIMA参数化用向量构建图节点，而非常规的空间拓扑结构，以此找出疫情数据背后的交互拓扑结构。实验结果表明，IT-GCN能够捕捉全面的交互-时间拓扑结构，并在美国实现对新冠疫情每日感染病例的良好短期预测。在平均绝对误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）方面，我们的框架优于现有最先进的基线模型。我们认为，IT-GCN是预测新冠疫情每日感染病例及其他相关时间序列的一种有效且合理的方法。此外，该预测有助于改进防控政策。
