mlsanityData to 14 Oct 2026

Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

arXiv 2007.03113 ↗First posted 6 Jul 2020cs.LG cs.SIcited by 64

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Abstract (arXiv)

In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting models, the proposed approach learns from a single large-scale spatio-temporal graph, where nodes represent the region-level human mobility, spatial edges represent the human mobility based inter-region connectivity, and temporal edges represent node features through time. We evaluate this approach on the US county level COVID-19 dataset, and demonstrate that the rich spatial and temporal information leveraged by the graph neural network allows the model to learn complex dynamics. We show a 6% reduction of RMSLE and an absolute Pearson Correlation improvement from 0.9978 to 0.998 compared to the best performing baseline models. This novel source of information combined with graph based deep learning approaches can be a powerful tool to understand the spread and evolution of COVID-19. We encourage others to further develop a novel modeling paradigm for infectious disease based on GNNs and high resolution mobility data.

Key facts. Cited by 64 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jul 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 13; 2022: 15; 2023: 17; 2024: 9; 2025: 6; 2026: 4.

64 papers.

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  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  3. 2021Automated Machine Learning on Graphs: A Surveycited by 39
  4. 2022Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networkscited by 34
  5. 2024A Survey of Deep Learning and Foundation Models for Time Series Forecastingcited by 33
  6. 2023Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoderscited by 32
  7. 2024A Review of Graph Neural Networks in Epidemic Modelingcited by 31
  8. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  9. 2021A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Predictioncited by 28
  10. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  11. 2023Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalizationcited by 26
  12. 2021Heterogeneous Temporal Graph Neural Networkcited by 23
38 more
  1. 2023Spatio-Temporal Graph Neural Networks: A Surveycited by 23
  2. 2022Graph Neural Networks Designed for Different Graph Types: A Surveycited by 21
  3. 2022TF-GNN: Graph Neural Networks in TensorFlowcited by 21
  4. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  5. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  6. 2023Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulationcited by 15
  7. 2022Temporal Multiresolution Graph Neural Networks For Epidemic Predictioncited by 11
  8. 2021Robust Graph Neural Networks via Probabilistic Lipschitz Constraintscited by 10
  9. 2021Estimating the State of Epidemics Spreading with Graph Neural Networkscited by 9
  10. 2022Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directionscited by 9
  11. 2022Data-Centric Epidemic Forecasting: A Surveycited by 8
  12. 2021Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germanycited by 7
  13. 2024Expressive Power of Temporal Message Passingcited by 7
  14. 2023Graph Neural Network for spatiotemporal data: methods and applicationscited by 6
  15. 2023MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecastingcited by 6
  16. 2022Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-seriescited by 5
  17. 2023MPSTAN: Metapopulation-based Spatio-Temporal Attention Network for Epidemic Forecastingcited by 5
  18. 2023Revealing the Power of Masked Autoencoders in Traffic Forecastingcited by 5
  19. 2021Early Detection of COVID-19 Hotspots Using Spatio-Temporal Datacited by 3
  20. 2021Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Datacited by 3
  21. 2022Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theorycited by 3
  22. 2023Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectorscited by 3
  23. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 3
  24. 2021Machine learning applications for COVID-19: A state-of-the-art reviewcited by 2
  25. 2021Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility informationcited by 2
  26. 2024Convection-Diffusion Equation: A Theoretically Certified Framework for Neural Networkscited by 2
  27. 2025The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logicscited by 2
  28. 2021Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Networkcited by 1
  29. 2021Improving COVID-19 Forecasting using eXogenous Variablescited by 1
  30. 2022Fine-Grained Population Mobility Data-Based Community-Level COVID-19 Prediction Modelcited by 1
  31. 2022Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Networkcited by 1
  32. 2024Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Responsecited by 1
  33. 2025Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reportscited by 1
  34. 2026Prior Knowledge-enhanced Spatio-temporal Epidemic Forecastingcited by 1
  35. 2021STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Managementcited by 0
  36. 2022Temporal-Spatial dependencies ENhanced deep learning model (TSEN) for household leverage series forecastingcited by 0
  37. 2022Deep Learning-Derived Optimal Aviation Strategies to Control Pandemicscited by 0
  38. 2022Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecastingcited by 0

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