Abstract
We consider a data set of locations where people in Central
Bohemia have been infected by tick-borne encephalitis (TBE), and where
population census data and covariates concerning vegetation and
altitude are available. The aims are to estimate the risk map of the
disease and to study the dependence of the risk on the
covariates. Instead of using the common areal level approaches we base
the analysis on a Bayesian approach for a log Gaussian Cox point
process with covariates. Posterior characteristics for a discretized
version of the log Gaussian Cox process are computed using Markov
chain Monte Carlo methods. A particular problem which is thoroughly
discussed is to determine a model for the background population
density. The risk map shows a clear dependency with the population
intensity models and the basic model which is adopted for the
population intensity determines what covariates influence the risk of
TBE. Model validation is based on the posterior predictive
distribution of various summary statistics.
Originalsprog | Engelsk |
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Tidsskrift | Image Analysis and Stereology |
Vol/bind | 24 |
Sider (fra-til) | 1-10 |
Antal sider | 10 |
ISSN | 1580-3139 |
Status | Udgivet - 2005 |