论文标题
Epignn：通过图神经网络探索空间传输，以进行区域流行预测
EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
论文作者
论文摘要
流行病预测是有效控制流行病的关键，并帮助世界减轻威胁公共卫生的危机。为了更好地理解流行病的传播和演变，我们提出了基于图神经网络的基于图的流行病预测模型Epignn。具体而言，我们设计了一个传输风险编码模块，以表征区域在流行过程中的局部和全局空间效应，并将其纳入模型。同时，我们开发了一个区域感知的图形学习者（RAGL），该图形将传播风险，地理依赖性和时间信息考虑在内，以更好地探索时空依赖性，并使地区意识到相关地区的流行状况。 RAGL还可以与外部资源（例如人类流动性）相结合，以进一步提高预测性能。对五个现实世界流行有关的数据集（包括流感和Covid-19）进行了全面的实验证明了我们提出的方法的有效性，并表明Epignn在RMSE中优于最先进的基线。
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.
