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.
Related information
Rebecca O'Leary, Research collaborator
http://goo.gl/LqhdR6
Access the data
Licence
Copyright
© 2011 Hu et al.
Dates of data collection
From 2001 to 2011
Connections
Contacts
Other
Date record created:
2014-07-03T16:26:55
Date record modified:
2019-07-04T10:53:59
Record status:
Published - Open Access