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

Published: 20 Oct 2023, Last Modified: 05 Nov 2023TGL Workshop 2023 LongPaperEveryoneRevisionsBibTeX
Keywords: Temporal graph neural networks, multivariate timeseries, pandemic forecasting, epidemic modeling
TL;DR: We propose Attention-based Multiresolution Graph Neural Networks, a novel spatio-temporal model, that can capture the multiscale structures of the epidemic network for pandemic forecasting.
Abstract: 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. Our data and source code are publicly available at
Format: Long paper, up to 8 pages. If the reviewers recommend it to be changed to a short paper, I would be willing to revise my paper to fit within 4 pages.
Submission Number: 11