A statistical framework for the analysis of multivariate infectious disease surveillance data

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Discussion Paper ; No. 402

Classification and Topics

• Subject: Branching Process with Immigration ; Infectious Disease Surveillance ; Maximum Likelihood ; Multivariate Time Series of Counts ; Observation-driven ; Parameter-driven ; Space-Time-Models

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Held, Leonhard ; Höhle, Michael ; Hofmann, Mathias
• Event: Veröffentlichung
• (who): Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
• (where): München
• (when): 2004

Further information

• DOI: doi:10.5282/ubm/epub.1772
• Handle: http://hdl.handle.net/10419/31020
• URN: urn:nbn:de:bvb:19-epub-1772-2
• Last update: 10.03.2025, 11:42 AM CET