Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmission and evolution of epidemics, we propose EpiGNN, a graph neural network-based model for epidemic forecasting. Specifically, we design a transmission risk encoding module to characterize local and global spatial effects of regions in epidemic processes and incorporate them into the model. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takes transmission risk, geographical dependencies, and temporal information into account to better explore spatial-temporal dependencies and makes regions aware of related regions' epidemic situations. The RAGL can also combine with external resources, such as human mobility, to further improve prediction performance. Comprehensive experiments on five real-world epidemic-related datasets (including influenza and COVID-19) demonstrate the effectiveness of our proposed method and show that EpiGNN outperforms state-of-the-art baselines by 9.48% in RMSE.

提出了一种基于图神经网络的流行病预测模型EpiGNN，该模型通过传播风险编码、区域感知图学习器和外部资源融合等操作，能够更好地探索流行病的时空依赖关系，提高预测效果。在5个真实流行病数据集上的实验结果表明，EpiGNN 模型的 RMSE 表现比现有模型均有 9.48% 的提高。

利用图神经网络探索空间传播进行地区疫情预测