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  • Bayesian Classification and regression trees for predicting incidence of cryptosporidiosis

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    Author(s)
    Hu, Wenbiao
    O'Leary, Rebecca A
    Mengersen, Kerrie
    Choy, Samantha Low
    Griffith University Author(s)
    Low-Choy, Sama J.
    Year published
    2011
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    Abstract
    Background: 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. Methodology/Principal Findings: 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 ...
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    Background: 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. Methodology/Principal Findings: 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. Conclusions/Significance: 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.
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    Journal Title
    PLoS One
    Volume
    6
    Issue
    8
    DOI
    https://doi.org/10.1371/journal.pone.0023903
    Copyright Statement
    © 2011 Hu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Subject
    Applied statistics
    Publication URI
    http://hdl.handle.net/10072/173221
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    • Journal articles

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