A stochastic model for multivariate surveillance of infectious diseases

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

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 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.

• Item Type: Paper (Research Paper)
• Collections: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English