Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/31020 
Erscheinungsjahr: 
2004
Schriftenreihe/Nr.: 
Discussion Paper No. 402
Verlag: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Zusammenfassung: 
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.
Schlagwörter: 
Branching Process with Immigration
Infectious Disease Surveillance
Maximum Likelihood
Multivariate Time Series of Counts
Observation-driven
Parameter-driven
Space-Time-Models
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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