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.

翻译：准确的流行病预测是控制疾病传播的关键任务。许多基于深度学习的模型在构建空间信息时仅关注静态图或动态图，忽略了它们之间的关系。此外，这些模型通常依赖循环结构，可能导致误差累积和计算耗时。为解决上述问题，我们提出了一种名为基于骨干的动态图时空网络（BDGSTN）的新型模型。直观上，图结构的连续平滑变化使得相邻图结构共享基本模式。为捕捉这一特性，我们采用自适应方法生成包含主要信息的静态骨干图，并通过时序模型生成流行病数据的动态时序图，将两者融合形成基于骨干的动态图。为克服循环结构可能带来的局限，我们引入线性模型DLinear处理时间依赖性，并将其与动态图卷积结合进行流行病预测。在两个数据集上的大量实验表明，BDGSTN优于基线模型，消融对比进一步验证了模型组件的有效性。此外，我们通过不同方面的信息度量指标分析并衡量了骨干图与时序图的重要性。最后，我们比较模型参数规模和训练时间，确认了BDGSTN卓越的复杂性和效率。