Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence—Belgium as a study case

Abstract 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 framewor... Mehr ...

Verfasser: Simon Dellicour
Catherine Linard
Nina Van Goethem
Daniele Da Re
Jean Artois
Jérémie Bihin
Pierre Schaus
François Massonnet
Herman Van Oyen
Sophie O. Vanwambeke
Niko Speybroeck
Marius Gilbert
Dokumenttyp: Artikel
Erscheinungsdatum: 2021
Reihe/Periodikum: International Journal of Health Geographics, Vol 20, Iss 1, Pp 1-11 (2021)
Verlag/Hrsg.: BMC
Schlagwörter: COVID-19 / Hospitalisation incidence / Spatial covariates / Temporal covariates / Boosted regression trees / Belgium / Computer applications to medicine. Medical informatics / R858-859.7
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-26581935
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://doi.org/10.1186/s12942-021-00281-1