Backbone-based dynamic spatio-temporal graph neural network for epidemic forecasting

Accurate epidemic forecasting is a critical task in controlling epidemic spread. Many deep learning-based models focus only on static or dynamic graphs when dealing with spatial information, ignoring their relationship. Additionally, these models often rely on recurrent structures, which can lead to error accumulation and computational time consumption. To address the aforementioned problems, we propose a novel model called Backbone-based Dynamic Spatio-Temporal Graph Neural Network (BDSTGNN). Intuitively, the continuous and smooth changes in graph structure make adjacent graph structures share a basic pattern. To capture this property, we use adaptive methods to generate static backbone graphs containing the primary information, and use temporal models to generate dynamic temporal graphs, and then fuse them to generate a backbone-based dynamic graph. To overcome potential limitations associated with recurrent structures, we introduce a linear model DLinear to handle temporal dependencies, and combine it with dynamic graph convolution for epidemic forecasting. Extensive experiments on two datasets demonstrate that BDSTGNN outperforms baseline models, and ablation comparison further verifies the effectiveness of model components. Furthermore, we analyze and measure the significance of backbone and temporal graphs by using information metrics from different aspects. Finally, we verify the superior efficiency of the BDSTGNN.