mlsanityData to 14 Oct 2026

Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study

arXiv 2305.07731 ↗First posted 12 May 2023cs.LG physics.soc-phcited by 6

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Abstract (arXiv)

Modeling and simulations of pandemic dynamics play an essential role in understanding and addressing the spreading of highly infectious diseases such as COVID-19. In this work, we propose a novel deep learning architecture named Attention-based Multiresolution Graph Neural Networks (ATMGNN) that learns to combine the spatial graph information, i.e. geographical data, with the temporal information, i.e. timeseries data of number of COVID-19 cases, to predict the future dynamics of the pandemic. The key innovation is that our method can capture the multiscale structures of the spatial graph via a learning to cluster algorithm in a data-driven manner. This allows our architecture to learn to pick up either local or global signals of a pandemic, and model both the long-range spatial and temporal dependencies. Importantly, we collected and assembled a new dataset for New Zealand. We established a comprehensive benchmark of statistical methods, temporal architectures, graph neural networks along with our spatio-temporal model. We also incorporated socioeconomic cross-sectional data to further enhance our prediction. Our proposed model have shown highly robust predictions and outperformed all other baselines in various metrics for our new dataset of New Zealand along with existing datasets of England, France, Italy and Spain. For a future work, we plan to extend our work for real-time prediction and global scale. Our data and source code are publicly available at https://github.com/HySonLab/pandemic_tgnn

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 26 of its 71 references that are in the map. First posted on arXiv 12 May 2023.

Builds on

26 of 71 references.

References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  5. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  6. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  7. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  8. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  9. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  10. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  11. 2015Gated Graph Sequence Neural Networkscited by 796
  12. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 754
14 more
  1. 2018Deep learning for time series classification: a reviewcited by 576
  2. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  3. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 509
  4. 2016Learning Convolutional Neural Networks for Graphscited by 314
  5. 2016Molecular Graph Convolutions: Moving Beyond Fingerprintscited by 238
  6. 2014A* Samplingcited by 151
  7. 2019Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networkscited by 103
  8. 2018Covariant Compositional Networks For Learning Graphscited by 54
  9. 2022A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metricscited by 26
  10. 2021Multiresolution Equivariant Graph Variational Autoencodercited by 20
  11. 2021Deep learning via LSTM models for COVID-19 infection forecasting in Indiacited by 12
  12. 2022Temporal Multiresolution Graph Neural Networks For Epidemic Predictioncited by 11
  13. 2021A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USAcited by 7
  14. 2023Fast Temporal Wavelet Graph Neural Networkscited by 6

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Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024A Review of Graph Neural Networks in Epidemic Modelingcited by 31
  2. 2024TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understandingcited by 3
  3. 2025Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEscited by 3
  4. 2025H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemiccited by 1
  5. 2024Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary Metaheuristicscited by 0
  6. 2024Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecastingcited by 0