Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence—Belgium as a study case
Background: The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection. Methods: To overcome these limitations, we propose an analytical framework to in... Mehr ...
Verfasser: |
Dellicour, Simon
Linard, Catherine
Van Goethem, Nina
Da Re, Daniele
Artois, Jean
Bihin, Jérémie
Schaus, Pierre
Massonnet, François
Van Oyen, Herman
Vanwambeke, Sophie O.
Speybroeck, Niko
Gilbert, Marius |
Dokumenttyp: |
Artikel |
Erscheinungsdatum: |
2021 |
Reihe/Periodikum: |
Dellicour , S , Linard , C , Van Goethem , N , Da Re , D , Artois , J , Bihin , J , Schaus , P , Massonnet , F , Van Oyen , H , Vanwambeke , S O , Speybroeck , N & Gilbert , M 2021 , ' Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence—Belgium as a study case ' , International Journal of Health Geographics , vol. 20 , no. 1 , 29 . https://doi.org/10.1186/s12942-021-00281-1 |
Schlagwörter: |
Belgium
/ Boosted regression trees
/ COVID-19
/ Hospitalisation incidence
/ Spatial covariates
/ Temporal covariates |
Sprache: |
Englisch |
Permalink: |
https://search.fid-benelux.de/Record/base-28553658 |
Datenquelle: |
BASE;
Originalkatalog |
Powered By: |
BASE |
Link(s) :
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https://researchportal.unamur.be/en/publications/1cbb91bc-c6be-45f6-af56-88ffdebddd31 |