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

Master thesis (2023)

Authors

V.M. Croft Mechanical Engineering

Contributors

C. Della Santina Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (mentor)
R. Babuska Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (graduation committee member)
S.C.J.L. van Iersel Rijksinstituut voor Volksgezondheid en Milieu (RIVM) (mentor)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2023 Maxime Croft
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Published Date

11-01-2023

Language

English

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Faculty

Mechanical Engineering

Abstract

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 municipalities over time using Gated Recurrent Units (GRUs) and the spatial interactions between municipalities using GATv2 layers. To the best of our knowledge, this model is the first to incorporate sewage data, the stringency index, and commuting information into GNN-based infection prediction.
In experiments on the developed real-world dataset, we demonstrate that the model outperforms simple baselines and purely spatial or temporal models for the COVID-19 wild type, alpha, and delta variants. In combination with an average R2 of 0.795 for forecasting infections and of 0.899 for predicting the associated trend of these variants, we conclude that the model is well suited for predicting the spread of infectious diseases with similar disease dynamics in real world applications. To increase prediction performance and to improve the generalizability of the model for infectious diseases with more complex disease
dynamics, we recommend using additional (synthetic) data or expanding the regional forecasting scale in future work.