EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
This work addresses epidemic forecasting for public health management, but it is incremental as it builds on existing graph neural network methods with specific enhancements.
The paper tackled regional epidemic forecasting by proposing EpiGNN, a graph neural network model that incorporates spatial transmission risks and dependencies, resulting in a 9.48% improvement in RMSE over state-of-the-art baselines on datasets including influenza and COVID-19.
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.