52 KB428 KB35 KB200020242005Beneš, ViktorBodlák, KarelMoller, JesperWaagepetersen, Rasmus Plenge10 straniletnik:24številka:3str. 159-168ISSN:1580-3139COBISSID:20441561URN:URN:NBN:SI:doc-QXIHYJUQenDruštvo za stereologijo in kvantitativno analizo slike, Medicinska fakultetaImage analysis and stereologyBayes TheoremBayesov teorembolezniCzech RepublicČeška republikaDejavniki tveganjadiseaseEncefalitis klopniEncephalitis, Tick-BorneEpidemiologykartiranjeklopni encefalitisNormal DistributionNormalna distribucijaRisk FactorsA case study on point process modelling in disease mapping|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 area 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 forthe 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 statisticsTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaDruštvo za stereologijo in kvantitativno analizo slike