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

arXiv: https://arxiv.org/abs/2208.11517 · HTML: https://mlsanity.com/paper/2208.11517/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018)
- [Inter-Series Attention Model for COVID-19 Forecasting](https://mlsanity.com/paper/2010.13006/) (2020)

## Built on by (most cited first, 17 of 17)

- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Hard Sample Aware Network for Contrastive Deep Graph Clustering](https://mlsanity.com/paper/2212.08665/) (2022)
- [Cluster-guided Contrastive Graph Clustering Network](https://mlsanity.com/paper/2301.01098/) (2023)
- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph](https://mlsanity.com/paper/2410.00049/) (2024)
- [MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting](https://mlsanity.com/paper/2308.15840/) (2023)
- [Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics](https://mlsanity.com/paper/2603.07108/) (2026)
- [Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy](https://mlsanity.com/paper/2404.01217/) (2024)
- [SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting](https://mlsanity.com/paper/2605.06530/) (2026)
- [Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting](https://mlsanity.com/paper/2606.19560/) (2026)
- [Machine Learning for Infectious Disease Risk Prediction: A Survey](https://mlsanity.com/paper/2308.03037/) (2023)
- [Cooperative Network Learning for Large-Scale and Decentralized Graphs](https://mlsanity.com/paper/2311.02117/) (2023)
- [Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting](https://mlsanity.com/paper/2411.17372/) (2024)
- [Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction](https://mlsanity.com/paper/2603.19599/) (2026)
- [Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance](https://mlsanity.com/paper/2605.18793/) (2026)
- [TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting](https://mlsanity.com/paper/2607.26854/) (2026)
- [TERN: A Delta-rule Memory with a Seasonal Reference and Online Adaptation for Epidemic Forecasting](https://mlsanity.com/paper/2609.18407/) (2026)

## Abstract (arXiv)

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.