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Christel Faes
Researcher at University of Hasselt
Publications - 231
Citations - 4423
Christel Faes is an academic researcher from University of Hasselt. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 30, co-authored 196 publications receiving 3426 citations. Previous affiliations of Christel Faes include Katholieke Universiteit Leuven.
Papers
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Journal ArticleDOI
Comparison of different software implementations for spatial disease mapping.
TL;DR: This paper investigates CAR models typically used in disease mapping for spatially discrete count data and provides an in-depth comparison between analysis results, coming from R-packages CARBayes, R2OpenBUGS, NIMBLE, R 2BayesX, R-INLA, and RStan.
Journal ArticleDOI
COVID-19 mortality, excess mortality, deaths per million and infection fatality ratio, Belgium, 9 March 2020 to 28 June 2020
Geert Molenberghs,Christel Faes,Johan Verbeeck,Patrick Deboosere,Steven Abrams,Lander Willem,Jan Aerts,Heidi Theeten,Brecht Devleesschauwer,Natalia Bustos Sierra,Françoise Renard,Sereina A. Herzog,Patrick Lusyne,Johan Van der Heyden,Herman Van Oyen,Pierre Van Damme,Niel Hens +16 more
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Journal ArticleDOI
Spatial determination of successive spikes in the isolated cat duodenum
Wim J. E. P. Lammers,Christel Faes,Betty Stephen,Luc Bijnens,L. Ver Donck,J. A. J. Schuurkes +5 more
TL;DR: In seven isolated segments of the feline duodenum, spikes and spike patches tend to occur significantly in some areas and not in others, which will play a role in intestinal motility.
Journal ArticleDOI
Estimating herd prevalence on the basis of aggregate testing of animals
TL;DR: In this paper, an improved version of the manuscript is presented, which was supported by the Interuniversity Attraction Pole research network P6/03 of the Belgian Government (Belgian Science Policy) and Research Foundation Flanders.
Book ChapterDOI
Enhancing Discovered Process Models Using Bayesian Inference and MCMC.
TL;DR: In this paper, Bayesian inference and Markov Chain Monte Carlo is used to build a statistical model on top of a process model using event data, which is able to generate probability distributions for choices in a process' control-flow.