title: A two-component model for counts of infectious diseases.
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 parameter-driven component relates the disease incidence to latent parameters describing endemic seasonal patterns, which are typical for infectious disease surveillance data. An 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 the Bayesian model averaging using Markov chain Monte Carlo techniques. We illustrate our approach through analysis of simulated data and real notification data obtained from the German infectious disease surveillance system, administered by the Robert Koch Institute in Berlin. Software to fit the proposed model can be obtained from http://www.statistik.lmu.de/ approximately mhofmann/twins.
authors.affiliation: Department of Statistics, Ludwig-Maximilians-Universität München, Ludwigstrasse 33, 80539 München, Germany. leonhard.held@stat.uni-muenchen.de
authors.forename: Leonhard
authors.initials: L
authors.lastname: Held
authors.forename: Mathias
authors.initials: M
authors.lastname: Hofmann
authors.forename: Michael
authors.initials: M
authors.lastname: Höhle
authors.forename: Volker
authors.initials: V
authors.lastname: Schmid
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003141:Communicable Diseases / Q000453:epidemiology*; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D005858:Germany / Q000453:epidemiology; D006506:Hepatitis A / Q000453:epidemiology*; D006509:Hepatitis B / Q000453:epidemiology* / Q000517:prevention & control; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D011159:Population Surveillance; D012621:Seasons; D013269:Stochastic Processes; D014611:Vaccination / Q000706:statistics & numerical data