# A Review of Graph Neural Networks in Epidemic Modeling

arXiv: https://arxiv.org/abs/2403.19852 · HTML: https://mlsanity.com/paper/2403.19852/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 144 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [Graph Structure Learning for Robust Graph Neural Networks](https://mlsanity.com/paper/2005.10203/) (2020)
- [GRAND: Graph Neural Diffusion](https://mlsanity.com/paper/2106.10934/) (2021)
- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [Graph Neural Ordinary Differential Equations](https://mlsanity.com/paper/1911.07532/) (2019)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited](https://mlsanity.com/paper/2202.03580/) (2022)
- [Towards Self-Explainable Graph Neural Network](https://mlsanity.com/paper/2108.12055/) (2021)
- [Elastic Graph Neural Networks](https://mlsanity.com/paper/2107.06996/) (2021)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [Generalization Analysis of Message Passing Neural Networks on Large Random Graphs](https://mlsanity.com/paper/2202.00645/) (2022)
- [Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2010.05313/) (2020)
- [Attention Mechanism in Neural Networks: Where it Comes and Where it Goes](https://mlsanity.com/paper/2204.13154/) (2022)
- [Graph Neural Networks for Multimodal Single-Cell Data Integration](https://mlsanity.com/paper/2203.01884/) (2022)
- [EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting](https://mlsanity.com/paper/2208.11517/) (2022)
- [Deep learning of contagion dynamics on complex networks](https://mlsanity.com/paper/2006.05410/) (2020)
- [Finding Patient Zero: Learning Contagion Source with Graph Neural Networks](https://mlsanity.com/paper/2006.11913/) (2020)
- [Modelling transmission and control of the COVID-19 pandemic in Australia](https://mlsanity.com/paper/2003.10218/) (2020)
- [Reinforced Epidemic Control: Saving Both Lives and Economy](https://mlsanity.com/paper/2008.01257/) (2020)
- [Estimating the State of Epidemics Spreading with Graph Neural Networks](https://mlsanity.com/paper/2105.05060/) (2021)
- [Integrating LSTMs and GNNs for COVID-19 Forecasting](https://mlsanity.com/paper/2108.10052/) (2021)
- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)

## Built on by (most cited first, 30 of 31)

- [Edge Prompt Tuning for Graph Neural Networks](https://mlsanity.com/paper/2503.00750/) (2025)
- [Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks](https://mlsanity.com/paper/2503.23167/) (2025)
- [Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness](https://mlsanity.com/paper/2407.12068/) (2024)
- [Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph](https://mlsanity.com/paper/2410.00049/) (2024)
- [Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers](https://mlsanity.com/paper/2406.19258/) (2024)
- [Mitigating Propensity Bias of Large Language Models for Recommender Systems](https://mlsanity.com/paper/2409.20052/) (2024)
- [GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights](https://mlsanity.com/paper/2406.16715/) (2024)
- [S2FGL: Spatial Spectral Federated Graph Learning](https://mlsanity.com/paper/2507.02409/) (2025)
- [A Comprehensive Data-centric Overview of Federated Graph Learning](https://mlsanity.com/paper/2507.16541/) (2025)
- [Scalable Graph Condensation with Evolving Capabilities](https://mlsanity.com/paper/2502.17614/) (2025)
- [EpiLearn: A Python Library for Machine Learning in Epidemic Modeling](https://mlsanity.com/paper/2406.06016/) (2024)
- [TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks](https://mlsanity.com/paper/2407.08064/) (2024)
- [Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics](https://mlsanity.com/paper/2603.07108/) (2026)
- [Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes](https://mlsanity.com/paper/2502.03393/) (2025)
- [Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing](https://mlsanity.com/paper/2505.09702/) (2025)
- [Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis](https://mlsanity.com/paper/2602.12373/) (2026)
- [Large Generative Graph Models](https://mlsanity.com/paper/2406.05109/) (2024)
- [Learning production functions for supply chains with graph neural networks](https://mlsanity.com/paper/2407.18772/) (2024)
- [H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic](https://mlsanity.com/paper/2503.10907/) (2025)
- [Flow-Attentional Graph Neural Networks](https://mlsanity.com/paper/2506.06127/) (2025)
- [SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting](https://mlsanity.com/paper/2605.06530/) (2026)
- [Graphlets correct for the topological information missed by random walks](https://mlsanity.com/paper/2405.14194/) (2024)
- [TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data](https://mlsanity.com/paper/2408.16379/) (2024)
- [Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting](https://mlsanity.com/paper/2411.17372/) (2024)
- [BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks](https://mlsanity.com/paper/2412.11964/) (2024)
- [Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks](https://mlsanity.com/paper/2501.02043/) (2025)
- [Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approach](https://mlsanity.com/paper/2507.10014/) (2025)
- [Epi2-Net: Advancing Epidemic Dynamics Forecasting with Physics-Inspired Neural Networks](https://mlsanity.com/paper/2508.02049/) (2025)
- [Mitigating Bias in Graph Hyperdimensional Computing](https://mlsanity.com/paper/2512.07433/) (2025)
- [Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks](https://mlsanity.com/paper/2602.02763/) (2026)

## 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.