Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31020
Authors: 
Held, Leonhard
Höhle, Michael
Hofmann, Mathias
Year of Publication: 
2004
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 402
Abstract: 
A framework for the statistical analysis of counts from infectious disease surveillance database is proposed. In its simplest form, the model can be seen as a Poisson branching process model with immigration. Extensions to include seasonal effects, time trends and overdispersion are outlined. The model is shown to provide an adequate fit and reliable one-step-ahead prediction intervals for a typical infectious disease surveillance time series. Furthermore, a multivariate formulation is proposed, which is well suited to capture space-time interactions caused by the spatial spread of a disease over time. analyses of uni- and multivariate times series on several infectious diseases are described. All analyses have been done using general optimization routines where ML estimates and corresponding standard errors are readily available.
Subjects: 
Branching Process with Immigration
Infectious Disease Surveillance
Maximum Likelihood
Multivariate Time Series of Counts
Observation-driven
Parameter-driven
Space-Time-Models
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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