Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis

IEEE Open Journal of Engineering in Medicine and Biology

Zehua Yu¹  Bowen Lu¹  Xutao Li¹  Zhulun Yang¹  Xianwei Zheng²  Maxian Fu³ 

[1] College of Engineering, Shantou University, Shantou, Guangdong, China;School of Mathematics and Big Data, Foshan University, Foshan, Guangdong, China;The Second Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou, Guangdong, China;

关键词: ARIMA;    Covid-19;    GCN;    time series prediction;    traffic forecasting;   

DOI  :  10.1109/OJEMB.2021.3063890

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown