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Hofmann, M.; Höhle, Michael and Held, Leonhard (2004): A stochastic model for multivariate surveillance of infectious diseases. Collaborative Research Center 386, Discussion Paper 394 [PDF, 296kB]


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.

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