A two-component model for counts of infectious diseases

Held, Leonhard; Hofmann, M.; Höhle, Michael und Schmid, Volker J. (2005): A two-component model for counts of infectious diseases. Sonderforschungsbereich 386, Discussion Paper 424

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 parameterdriven 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.

• Dokumententyp: Paper
• Keywords: Bayesian changepoint model; epidemic modelling; surveillance data; reversible jump Markov chain Monte Carlo
• Fakultät: Mathematik, Informatik und Statistik > Statistik > Sonderforschungsbereich 386 ; Sonderforschungsbereiche > Sonderforschungsbereich 386
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• Sprache: Englisch