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A Review of Graph Neural Networks in Epidemic Modeling

arXiv 2403.19852 ↗First posted 28 Mar 2024cs.LG cs.SI physics.soc-phcited by 31

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Abstract (arXiv)

Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Traditional mechanistic models mathematically describe the transmission mechanisms of infectious diseases. However, they often suffer from limitations of oversimplified or fixed assumptions, which could cause sub-optimal predictive power and inefficiency in capturing complex relation information. Consequently, Graph Neural Networks(GNNs) have emerged as a progressively popular tool in epidemic research. In this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic tasks and highlight potential future directions. To accomplish this objective, we introduce hierarchical taxonomies for both epidemic tasks and methodologies, offering a trajectory of development within this domain. For epidemic tasks, we establish a taxonomy akin to those typically employed within the epidemic domain. For methodology, we categorize existing work into Neural Models and Hybrid Models. Following this, we perform an exhaustive and systematic examination of the methodologies, encompassing both the tasks and their technical details. Furthermore, we discuss the limitations of existing methods from diverse perspectives and systematically propose future research directions. This survey aims to bridge literature gaps and promote the progression of this promising field, with a list of relevant papers at https://github.com/Emory-Melody/awesome-epidemic-modeling-papers. We hope that it will facilitate synergies between the communities of GNNs and epidemiology, and contribute to their collective progress.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 37 of its 144 references that are in the map. First posted on arXiv 28 Mar 2024.

Builds on

37 of 144 references.

References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2018Neural Ordinary Differential Equationscited by 3,733
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  7. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  8. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  9. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  10. 2021GRAND: Graph Neural Diffusioncited by 258
  11. 2022Graph neural networks for materials science and chemistrycited by 181
  12. 2019Graph Neural Ordinary Differential Equationscited by 153
25 more
  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2021Graph Neural Networks in Network Neurosciencecited by 91
  3. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  4. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  5. 2021Elastic Graph Neural Networkscited by 67
  6. 2020Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networkscited by 64
  7. 2022Generalization Analysis of Message Passing Neural Networks on Large Random Graphscited by 58
  8. 2020Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networkscited by 30
  9. 2022Attention Mechanism in Neural Networks: Where it Comes and Where it Goescited by 29
  10. 2022Graph Neural Networks for Multimodal Single-Cell Data Integrationcited by 18
  11. 2022EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecastingcited by 17
  12. 2020Deep learning of contagion dynamics on complex networkscited by 16
  13. 2020Finding Patient Zero: Learning Contagion Source with Graph Neural Networkscited by 15
  14. 2020Modelling transmission and control of the COVID-19 pandemic in Australiacited by 10
  15. 2021Estimating the State of Epidemics Spreading with Graph Neural Networkscited by 9
  16. 2020Reinforced Epidemic Control: Saving Both Lives and Economycited by 8
  17. 2021Integrating LSTMs and GNNs for COVID-19 Forecastingcited by 8
  18. 2022Data-Centric Epidemic Forecasting: A Surveycited by 8
  19. 2022Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomaxcited by 8
  20. 2020EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Controlcited by 6
  21. 2023Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's studycited by 6
  22. 2019A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecastingcited by 4
  23. 2023Globally Interpretable Graph Learning via Distribution Matchingcited by 3
  24. 2017Three faces of node importance in network epidemiology: Exact results for small graphscited by 2
  25. 2023Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagerycited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 13; 2025: 13; 2026: 5.

31 papers.

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  1. 2025Edge Prompt Tuning for Graph Neural Networkscited by 18
  2. 2025Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networkscited by 14
  3. 2024Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustnesscited by 11
  4. 2024Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graphcited by 10
  5. 2024Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformerscited by 9
  6. 2024Mitigating Propensity Bias of Large Language Models for Recommender Systemscited by 8
  7. 2024GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insightscited by 7
  8. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  9. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  10. 2025Scalable Graph Condensation with Evolving Capabilitiescited by 6
  11. 2024EpiLearn: A Python Library for Machine Learning in Epidemic Modelingcited by 5
  12. 2024TinyGraph: Joint Feature and Node Condensation for Graph Neural Networkscited by 5
19 more
  1. 2026Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemicscited by 4
  2. 2025Pre-training Epidemic Time Series Forecasters with Compartmental Prototypescited by 3
  3. 2025Enabling Group Fairness in Graph Unlearning via Bi-level Debiasingcited by 3
  4. 2026Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisiscited by 3
  5. 2024Large Generative Graph Modelscited by 2
  6. 2024Learning production functions for supply chains with graph neural networkscited by 2
  7. 2025H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemiccited by 1
  8. 2025Flow-Attentional Graph Neural Networkscited by 1
  9. 2026SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecastingcited by 1
  10. 2024Graphlets correct for the topological information missed by random walkscited by 0
  11. 2024TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Datacited by 0
  12. 2024Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecastingcited by 0
  13. 2024BetaExplainer: A Probabilistic Method to Explain Graph Neural Networkscited by 0
  14. 2025Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networkscited by 0
  15. 2025Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approachcited by 0
  16. 2025Epi2-Net: Advancing Epidemic Dynamics Forecasting with Physics-Inspired Neural Networkscited by 0
  17. 2025Mitigating Bias in Graph Hyperdimensional Computingcited by 0
  18. 2026Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attackscited by 0
  19. 2026Spatio-temporal stochastic graph-based learning for infectious disease forecastingcited by 0