mlsanityData to 7 Oct 2026

Estimating the State of Epidemics Spreading with Graph Neural Networks

arXiv 2105.05060 ↗First posted 10 May 2021q-bio.PE cs.LG physics.soc-phcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 17 of its 67 references that are in the map. First posted on arXiv 10 May 2021.

Builds on

17 of 67 references.

References · 17

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  6. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  7. 2017Deep learning for universal linear embeddings of nonlinear dynamicscited by 491
  8. 2019A Gentle Introduction to Deep Learning for Graphscited by 107
  9. 2020Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networkscited by 64
  10. 2016Network Topology Inference from Spectral Templatescited by 58
  11. 2014Epidemic processes in complex networkscited by 57
  12. 2016Semantic Object Parsing with Graph LSTMcited by 44
5 more
  1. 2020Reinforcement Learning for Optimization of COVID-19 Mitigation policiescited by 25
  2. 2020Finding Patient Zero: Learning Contagion Source with Graph Neural Networkscited by 15
  3. 2020Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networkscited by 4
  4. 2019Coevolution spreading in complex networkscited by 3
  5. 2020STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidencecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 2; 2026: 1.

9 papers.

  1. 2024A Review of Graph Neural Networks in Epidemic Modelingcited by 31
  2. 2023MPSTAN: Metapopulation-based Spatio-Temporal Attention Network for Epidemic Forecastingcited by 5
  3. 2021A Bayesian generative neural network framework for epidemic inference problemscited by 4
  4. 2024Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approachcited by 1
  5. 2022Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban areacited by 0
  6. 2023Deep Demixing: Reconstructing the Evolution of Network Epidemicscited by 0
  7. 2025Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approachcited by 0
  8. 2025GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interfacecited by 0
  9. 2026Spatio-temporal stochastic graph-based learning for infectious disease forecastingcited by 0