Within this paper spatial and spatio-temporal disease mapping models are reviewed and applied to reported Coxiellosis cases among cows in Switzerland, 2005-2008. Furthermore, an ecological regression is conducted using a linear and nonparametric association between the number of stillborn calves and the reported Coxiellosis cases within one Swiss region. As a tool for Bayesian inference integrated nested Laplace approximations (INLA) are used. INLA is a promising alternative to Markov chain Monte Carlo (MCMC) methods which is supposed to provide very accurate results within much less computational time. From a user's point of view INLA can easily be applied using an R package called INLA. Hence, it is shown how spatial and spatio-temporal models can be specied and run using INLA; some of the respective R code is also shown. Additionally, computational aspects ascomputational time, accuracy of the results and usability of the R packageare addressed. As a result it was found that the INLA package is easy to handle for this class of models although INLA is a very complex numerical algorithm. Furthermore, useful tools for model choice can be obtained as a standard output.