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A cluster model for space–time disease counts
Author(s) -
Yan Ping,
Clayton Murray K.
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2424
Subject(s) - reversible jump markov chain monte carlo , cluster analysis , computer science , markov chain monte carlo , inference , bayesian inference , data mining , bayesian probability , hierarchical clustering , cluster (spacecraft) , focus (optics) , parametric statistics , machine learning , artificial intelligence , statistics , mathematics , physics , optics , programming language
Abstract Modelling disease clustering over space and time can be helpful in providing indications of possible exposures and planning corresponding public health practices. Though a considerable number of studies focus on modelling spatio‐temporal patterns of disease, most of them do not directly model a spatio‐temporal clustering structure and could be ineffective for detecting clusters. In this paper, we extend a purely spatial cluster model to accommodate space–time clustering. Inference is performed in a Bayesian framework using reversible jump Markov chain Monte Carlo. This idea is illustrated using data on female breast cancer mortality from Japan. A hierarchical parametric space–time model for mapping disease is used for comparison. Copyright © 2006 John Wiley & Sons, Ltd.