Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A
  New Zealand's study
Viet Bach Nguyen Truong Son Hy Long Tran-Thanh Nhung Nghiem
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