mlsanityData to 7 Oct 2026

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

arXiv 2208.11517 ↗First posted 23 Aug 2022q-bio.QM cs.AI cs.LGcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 23 Aug 2022.

Builds on

8 of 22 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  4. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  5. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  6. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  7. 2018Temporal Pattern Attention for Multivariate Time Series Forecastingcited by 125
  8. 2020Inter-Series Attention Model for COVID-19 Forecastingcited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 4; 2025: 0; 2026: 7.

17 papers.

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  1. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  2. 2022Hard Sample Aware Network for Contrastive Deep Graph Clusteringcited by 61
  3. 2023Cluster-guided Contrastive Graph Clustering Networkcited by 60
  4. 2024A Review of Graph Neural Networks in Epidemic Modelingcited by 31
  5. 2024Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graphcited by 10
  6. 2023MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecastingcited by 5
  7. 2026Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemicscited by 4
  8. 2024Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancycited by 2
  9. 2026SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecastingcited by 1
  10. 2026Understanding Key Features of Time Series Foundation Models from Epidemic Forecastingcited by 1
  11. 2023Machine Learning for Infectious Disease Risk Prediction: A Surveycited by 0
  12. 2023Cooperative Network Learning for Large-Scale and Decentralized Graphscited by 0
5 more
  1. 2024Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecastingcited by 0
  2. 2026Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Predictioncited by 0
  3. 2026Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performancecited by 0
  4. 2026TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecastingcited by 0
  5. 2026TERN: A Delta-rule Memory with a Seasonal Reference and Online Adaptation for Epidemic Forecastingcited by 0