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

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.

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Year of publication:	2004
Authors:	Held, Leonhard ; Höhle, Michael ; Hofmann, Mathias
Publisher:	München : Techn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen
Subject:	Branching Process with Immigration \| Infectious Disease Surveillance \| Maximum Likelihood \| Multivariate Time Series of Counts \| Observation-driven \| Parameter-driven \| Space-Time-Models

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Series:	Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München ; 402
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	484051059 [GVK] hdl:10419/31020 [Handle]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010266152