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 ...
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Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2021 |
Reihe/Periodikum: | International Journal of Health Geographics, Vol 20, Iss 1, Pp 1-11 (2021) |
Verlag/Hrsg.: |
BMC
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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-27361194 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://doi.org/10.1186/s12942-021-00281-1 |