Integrating Transformer and GCN for COVID-19 Forecasting

Abstract

The spread of corona virus disease 2019 (COVID-19) has coincided with the rise of Transformer and graph neural networks, leading several studies to propose using them to better predict the evolution of a pandemic. The inconveniences of infectious diseases make it important to predict their spread. However, the single deep learning (DL) model has the problems of unstable prediction effect and poor convergence. When calculating the relationship between different positions within a sequence, Transformer does not consider the local context in which each position is located, which can make the prediction vulnerable to outliers, so the integration of the graph convolutional network (GCN) to capture local information is considered. In this paper, we use Transformer to encode the time sequence information of COVID-19 and GCN to decode the time sequence information with graph structure, so that Transformer and GCN are perfectly combined and spatial information is used to further study the integration of these two methods. In addition, we improve the traditional positional encoding structure and propose a dynamic positional encoding technique to extract dynamic temporal information effectively, which is proved to be the key to capture spatial and temporal patterns in data. To make our predictions more useful, we only focused on three states in the United States, covering one of the most affected states, one of the least affected states, and one intermediate state. We used mean absolute percentage error and mean square error as evaluation indexes. Experimental results show that the proposed time series model has better predictive performance than the current DL models. Moreover, the convergence of our model is also better than the current DL models, providing a more accurate reference for the prevention of epidemics.