# Estimating the State of Epidemics Spreading with Graph Neural Networks

arXiv: https://arxiv.org/abs/2105.05060 · HTML: https://mlsanity.com/paper/2105.05060/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 67 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Deep learning for universal linear embeddings of nonlinear dynamics](https://mlsanity.com/paper/1712.09707/) (2017)
- [A Gentle Introduction to Deep Learning for Graphs](https://mlsanity.com/paper/1912.12693/) (2019)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [Epidemic processes in complex networks](https://mlsanity.com/paper/1408.2701/) (2014)
- [Network Topology Inference from Spectral Templates](https://mlsanity.com/paper/1608.03008/) (2016)
- [Semantic Object Parsing with Graph LSTM](https://mlsanity.com/paper/1603.07063/) (2016)
- [Reinforcement Learning for Optimization of COVID-19 Mitigation policies](https://mlsanity.com/paper/2010.10560/) (2020)
- [Finding Patient Zero: Learning Contagion Source with Graph Neural Networks](https://mlsanity.com/paper/2006.11913/) (2020)
- [Coevolution spreading in complex networks](https://mlsanity.com/paper/1901.02125/) (2019)
- [Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks](https://mlsanity.com/paper/2011.09583/) (2020)
- [STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence](https://mlsanity.com/paper/2008.04215/) (2020)

## Built on by (most cited first, 9 of 9)

- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [A Bayesian generative neural network framework for epidemic inference problems](https://mlsanity.com/paper/2111.03383/) (2021)
- [MPSTAN: Metapopulation-based Spatio-Temporal Attention Network for Epidemic Forecasting](https://mlsanity.com/paper/2306.12436/) (2023)
- [Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach](https://mlsanity.com/paper/2410.23396/) (2024)
- [Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area](https://mlsanity.com/paper/2204.10752/) (2022)
- [Deep Demixing: Reconstructing the Evolution of Network Epidemics](https://mlsanity.com/paper/2306.07938/) (2023)
- [Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approach](https://mlsanity.com/paper/2507.10014/) (2025)
- [GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface](https://mlsanity.com/paper/2512.20813/) (2025)
- [Spatio-temporal stochastic graph-based learning for infectious disease forecasting](https://mlsanity.com/paper/2605.30662/) (2026)

## Abstract (arXiv)

When an epidemic spreads into a population, it is often unpractical or impossible to have a continuous monitoring of all subjects involved. As an alternative, algorithmic solutions can be used to infer the state of the whole population from a limited amount of measures. We analyze the capability of deep neural networks to solve this challenging task. Our proposed architecture is based on Graph Convolutional Neural Networks. As such it can reason on the effect of the underlying social network structure, which is recognized as the main component in the spreading of an epidemic. We test the proposed architecture with two scenarios modeled on the CoVid-19 pandemic: a generic homogeneous population, and a toy model of Boston metropolitan area.