- Prof Leonhard Held – Department of Statistics – Ludwing-Maximilians – University Munich
- FCUL – Campo Grande – Bloco C/6 – Piso 2 – Sala 6.2.53 -14:30 – 15h 45m
- Sexta-feira, 1 de Abril de 2005
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: The parameter-driven component relates the disease incidence to latent parameters describing endemic seasonal patterns, which are typical for infectious disease surveillance data. The observation-driven 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 very promising results.