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Epidemic forecasting is the key to effective control of epidemic transmissionand helps the world mitigate the crisis that threatens public health. To betterunderstand the transmission and evolution of epidemics, we propose EpiGNN, agraph neural network-based model for epidemic forecasting. Specifically, wedesign a transmission risk encoding module to characterize local and globalspatial effects of regions in epidemic processes and incorporate them into themodel. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takestransmission risk, geographical dependencies, and temporal information intoaccount to better explore spatial-temporal dependencies and makes regions awareof related regions' epidemic situations. The RAGL can also combine withexternal resources, such as human mobility, to further improve predictionperformance. Comprehensive experiments on five real-world epidemic-relateddatasets (including influenza and COVID-19) demonstrate the effectiveness ofour proposed method and show that EpiGNN outperforms state-of-the-art baselinesby 9.48% in RMSE.

EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting