A Model for COVID-19 Prediction Based on Spatio-temporal Convolutional Network

Abstract

COVID-19 has become a worldwide epidemic. Prediction of COVID-19 is an effective way to control its spread. Recently, some research efforts have made great progress on this task. However, these works rarely combine both the temporal and spatial domains for case number prediction. Moreover, most of them are only suitable for short-term prediction tasks, which cannot achieve good long-term predicting effects. Therefore, we use a method that combines human-mobility factors and time-series factors - the Spatio-temporal convolutional network (G-TCN) to deal with these problems. Firstly, we use data on the mobility of people between regions to generate graphs of regional relationships. Secondly, to process the spatial information at each moment, we apply multi-layer graph convolutional neural networks (GCNs) to aggregate multi-layer neighborhood information. And we input the information obtained by GCNs at different moments into temporal convolutional networks (TCNs), which are used to process the time-series information. Finally, we tested the proposed G-TCN method using datasets from four countries. The experimental results show that G-TCN has lower prediction errors than other comparison methods and can better fit the trend of COVID-19 development.