  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.
