Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/31133 
Year of Publication: 
2005
Series/Report no.: 
Discussion Paper No. 424
Publisher: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Abstract: 
We propose a stochastic model for the analysis of time series of disease counts as collected in typical surveillance systems on notifiable infectious diseases. The model is based on a Poisson or negative binomial observation model with two components: A parameter-driven component relates the disease incidence to latent parameters describing endemic seasonal patterns, which are typical for infectious disease surveillance data. A observationdriven or epidemic component is modeled with an autoregression on the number of cases at the previous time points. The autoregressive parameter is allowed to change over time according to a Bayesian changepoint model with unknown number of changepoints. Parameter estimates are obtained through Bayesian model averaging using Markov chain Monte Carlo (MCMC) techniques. In analyses of simulated and real datasets we obtain promising results.
Subjects: 
Bayesian changepoint model
epidemic modelling
surveillance data
reversible jump Markov chain Monte Carlo
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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