Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31026
Authors: 
Einbeck, Jochen
Tutz, Gerhard
Year of Publication: 
2004
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 395
Abstract: 
We describe a stochastic model based on a branching process for analyzing surveillance data of infectious diseases that allows to make forecasts of the future development of the epidemic. The model is based on a Poisson branching process with immigration with additional adjustment for possible overdispersion. An extension to a longitudinal model for the multivariate case is described. The model is estimated in a Bayesian context using Markov Chain Monte Carlo (MCMC) techniques. We illustrate the applicability of the model through analyses of simulated and real data.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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