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.

提出一种名为 BDGSTN 的新模型，通过生成骨干网络和动态图来预测流行病数据，使用线性模型和动态图卷积来处理时间依赖关系，实验证明该模型在准确预测流行病方面优于其他基准模型。

基于骨干的动态图空时网络用于疫情预测

Backbone-based Dynamic Graph Spatio-Temporal Network for Epidemic  Forecasting

Epidemic decision-making can effectively help the government to
comprehensively consider public security and economic development to respond to
public health and safety emergencies. Epidemic decision-making can effectively
help the government to comprehensively consider public security and economic
development to respond to public health and safety emergencies. Some studies
have shown that intensive learning can effectively help the government to make
epidemic decision, thus achieving the balance between health security and
economic development. Some studies have shown that intensive learning can
effectively help the government to make epidemic decision, thus achieving the
balance between health security and economic development. However, epidemic
data often has the characteristics of limited samples and high privacy.
However, epidemic data often has the characteristics of limited samples and
high privacy. This model can combine the epidemic situation data of various
provinces for cooperative training to use as an enhanced learning model for
epidemic situation decision, while protecting the privacy of data. The
experiment shows that the enhanced federated learning can obtain more optimized
performance and return than the enhanced learning, and the enhanced federated
learning can also accelerate the training convergence speed of the training
model. accelerate the training convergence speed of the client. At the same
time, through the experimental comparison, A2C is the most suitable
reinforcement learning model for the epidemic situation decision-making.
learning model for the epidemic situation decision-making scenario, followed by
the PPO model, and the performance of DDPG is unsatisfactory.

疫情决策的强化联邦学习模型可以帮助政府综合考虑公共安全与经济发展，实现健康安全与经济发展之间的平衡，同时保护数据隐私。