  Accurate epidemic forecasting is a critical task in controlling disease
transmission. Many deep learning-based models focus only on static or dynamic
graphs when constructing 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
Graph Spatio-Temporal Network (BDGSTN). 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 temporal models to
generate dynamic temporal graphs of epidemic data, fusing 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 BDGSTN
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 compare model parameter volume and training time
to confirm the superior complexity and efficiency of BDGSTN.
