A stochastic model for multivariate surveillance of infectious diseases

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 space-time 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.