Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.

<h4>Background</h4>Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART...

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Autores principales:	Wenbiao Hu, Rebecca A O'Leary, Kerrie Mengersen, Samantha Low Choy
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/32002d3458f541d99c36eabac32a9b95
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id	oai:doaj.org-article:32002d3458f541d99c36eabac32a9b95
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spelling	oai:doaj.org-article:32002d3458f541d99c36eabac32a9b952021-11-18T06:46:54ZBayesian classification and regression trees for predicting incidence of cryptosporidiosis.1932-620310.1371/journal.pone.0023903https://doaj.org/article/32002d3458f541d99c36eabac32a9b952011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21909377/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia.<h4>Methodology/principal findings</h4>We compared the results of a Bayesian CART model with those obtained using a Bayesian spatial conditional autoregressive (CAR) model. Overall, the analyses indicated that the nature and magnitude of the effect estimates were similar for the two methods in this study, but the CART model more easily accommodated higher order interaction effects.<h4>Conclusions/significance</h4>A Bayesian CART model for identification and estimation of the spatial distribution of disease risk is useful in monitoring and assessment of infectious diseases prevention and control.Wenbiao HuRebecca A O'LearyKerrie MengersenSamantha Low ChoyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 8, p e23903 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wenbiao Hu Rebecca A O'Leary Kerrie Mengersen Samantha Low Choy Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.
description	<h4>Background</h4>Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia.<h4>Methodology/principal findings</h4>We compared the results of a Bayesian CART model with those obtained using a Bayesian spatial conditional autoregressive (CAR) model. Overall, the analyses indicated that the nature and magnitude of the effect estimates were similar for the two methods in this study, but the CART model more easily accommodated higher order interaction effects.<h4>Conclusions/significance</h4>A Bayesian CART model for identification and estimation of the spatial distribution of disease risk is useful in monitoring and assessment of infectious diseases prevention and control.
format	article
author	Wenbiao Hu Rebecca A O'Leary Kerrie Mengersen Samantha Low Choy
author_facet	Wenbiao Hu Rebecca A O'Leary Kerrie Mengersen Samantha Low Choy
author_sort	Wenbiao Hu
title	Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.
title_short	Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.
title_full	Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.
title_fullStr	Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.
title_full_unstemmed	Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.
title_sort	bayesian classification and regression trees for predicting incidence of cryptosporidiosis.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/32002d3458f541d99c36eabac32a9b95
work_keys_str_mv	AT wenbiaohu bayesianclassificationandregressiontreesforpredictingincidenceofcryptosporidiosis AT rebeccaaoleary bayesianclassificationandregressiontreesforpredictingincidenceofcryptosporidiosis AT kerriemengersen bayesianclassificationandregressiontreesforpredictingincidenceofcryptosporidiosis AT samanthalowchoy bayesianclassificationandregressiontreesforpredictingincidenceofcryptosporidiosis
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Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.

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