Bayesianische Raum-Zeit-Modellierung in der Epidemiologie

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.