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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Yan P,Clayton MKdoi
10.1002/sim.2424subject
Has Abstractpub_date
2006-03-15 00:00:00pages
867-81issue
5eissn
0277-6715issn
1097-0258journal_volume
25pub_type
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