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 ...
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Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2023 |
Reihe/Periodikum: | Frontiers in Physics ; volume 11 ; ISSN 2296-424X |
Verlag/Hrsg.: |
Frontiers Media SA
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Sprache: | unknown |
Permalink: | https://search.fid-benelux.de/Record/base-27418163 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | http://dx.doi.org/10.3389/fphy.2023.1277052 |