A cluster model for space-time disease counts.

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 Med

journal_title

Statistics in medicine

authors

Yan P,Clayton MK

doi

10.1002/sim.2424

subject

Has Abstract

pub_date

2006-03-15 00:00:00

pages

867-81

issue

5

eissn

0277-6715

issn

1097-0258

journal_volume

25

pub_type

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