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

Held, Leonhard; Höhle, Michael; Hofmann, M. (2004): A statistical framework for the analysis of multivariate infectious disease surveillance data. Collaborative Research Center 386, Discussion Paper 402

Abstract

A framework for the statistical analysis of counts from infectious disease surveillance databases 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.

• Item Type: Paper (Research Paper)
• Keywords: Branching Process with Immigration; Infectious Disease Surveillance; Maximum Likelihood; Multivariate Time Series of Counts; Observation-driven; Parameter-driven; Space-Time-Models
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English