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Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis

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  • Wenbiao Hu
  • Rebecca A O'Leary
  • Kerrie Mengersen
  • Samantha Low Choy

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 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.

Suggested Citation

  • Wenbiao Hu & Rebecca A O'Leary & Kerrie Mengersen & Samantha Low Choy, 2011. "Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-8, August.
    • Handle: RePEc:plo:pone00:0023903
    DOI: 10.1371/journal.pone.0023903
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    References listed on IDEAS

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    2. Hoeting, Jennifer & Raftery, Adrian E. & Madigan, David, 1996. "A method for simultaneous variable selection and outlier identification in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 22(3), pages 251-270, July.
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