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, 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.
Dokumenttyp: Artikel
Erscheinungsdatum: 2021
Schlagwörter: Belgium / Boosted regression tree / COVID-19 / Hospitalisation incidence / Spatial covariate / Temporal covariates
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-27353231
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://hdl.handle.net/11572/408456