Forecasting infections with spatio-temporal graph neural networks: a case study of the Dutch SARS-CoV-2 spread

The spread of an epidemic over a population is influenced by a multitude of factors having both spatial and temporal nature, which are hard to completely capture using first principle methods. This paper concerns regional forecasting of SARS-Cov-2 infections 1 week ahead using machine learning. We especially focus on the Dutch case study for which we develop a municipality-level COVID-19 dataset. We propose to use a novel spatiotemporal graph neural network architecture to perform the predictions. The developed model captures the spread of infectious diseases within municipalities over time us... Mehr ...