This thesis is concerned with the analysis of spatial and temporal structures of epidemiological data using modern Bayes techniques. Mainly autoregressive distributions as Gaussian Markov random fields or random walks are used as smoothing priors.
Such extensive models can be estimated using MCMC methods only. Some effective algorithms are introduced to get estimates in acceptable time. Especially for space time interactions such algorithms are essential.
As example spatial Bayesian models are applied for wildlife disease incidence data. Discrete and continous frameworks for spatial analysis are compared on a data set on infant mortality cases. Age-period-cohort models are discussed in detail and an extension for spatial data is presented. Finally a stochastic model for space time data on infectious diseases is described.