A cluster model for space-time disease counts.
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.
Yan
Ping
P
Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, USA. yanp@stat.wisc.edu
Clayton
Murray K
MK
D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000453:epidemiology / Q000401:mortality; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks; D005260:Female; D006801:Humans; D007564:Japan / Q000453:epidemiology; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D013029:Space-Time Clustering