Bayesian classification and regression trees for predicting incidence of cryptosporidiosis: changes (%) in relative risks with 95% credible intervals from Bayesian spatiotemporal CAR models of cryptosporidiosis in Queensland, Australia

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This dataset was gathered to predict the spatial distribution of the cryptosporidiosis infection using selected social-ecological factors and climate variables. Predictions were completed using a Bayesian CART (Classification and Regression Tree) model.

The dataset presents the changes (%) in relative risks with 95% credible intervals from Bayesian spatio-temporal CAR models of cryptosporidiosis in Queensland, Australia.

Location of data collection

kmlPolyCoords

153.552920,-9.929730 137.994575,-9.929730 137.994575,-29.178588 153.552920,-29.178588 153.552920,-9.929730

Publications

Hu, Wen, O'Leary, Rebecca, Mengersen, Kerrie, & Low-Choy, Sama (2011) Bayesian Classification and regression trees for predicting incidence of cryptosporidiosis. PLoS ONE, 6(8), pp. 1-8. http://eprints.qut.edu.au/52459/

Research areas

Interval

Crystosporidiosis

Spatial distribution

Infectious diseases

Decision trees

Probability distribution

Bayes theorem

Risks

Credible

Spatio-temporal

Bayesian

Cryptosporidium

Cite this collection

Hu, Wenbiao; O'Leary, Rebecca A.; Mengersen, Kerrie; Choy, Samantha Low (2013): Changes (%) in relative risks with 95% credible intervals from Bayesian spatiotemporal CAR models of cryptosporidiosis in Queensland, Australia. Table_5.xls. PLOS ONE.