Forecasting of SARS-Cov-2 Infections within Dutch Municipalities using Spatio-Temporal Graph Neural Networks

This paper presents a novel approach to regional forecasting of SARS-Cov-2 infections one week ahead, which involves developing a municipality level COVID-19 dataset of the Netherlands and using a spatio-temporal graph neural network (GNN) to predict the number of infections. The developed model captures the spread of infectious diseases within...

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...