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Stochastic modelling of the spatial spread of influenza in Germany


Dargatz, C; Georgescu, V; Held, L (2006). Stochastic modelling of the spatial spread of influenza in Germany. Austrian Journal of Statistics, 35(1):5-20.

Abstract

In geographical epidemiology, disease counts are typically available in discrete spatial units and at discrete time-points. For example, surveillance data on infectious diseases usually consists of weekly counts of new infections in pre-defined geographical areas. Similarly, but on a different time-scale, cancer registries typically report yearly incidence or mortality counts in administrative regions. A major methodological challenge lies in building realistic models for space-time interactions on discrete irregular spatial graphs. In this paper, we will discuss an observation-driven approach, where past observed counts in neighbouring areas enter directly as explanatory variables, in contrast to the parameter-driven approach through latent Gaussian Markov random fields (Rue and Held, 2005) with spatio-temporal structure. The main focus will lie on the demonstration of the spread of influenza in Germany, obtained through the design and simulation of a spatial extension of the classical SIR model (Hufnagel et al., 2004).

Abstract

In geographical epidemiology, disease counts are typically available in discrete spatial units and at discrete time-points. For example, surveillance data on infectious diseases usually consists of weekly counts of new infections in pre-defined geographical areas. Similarly, but on a different time-scale, cancer registries typically report yearly incidence or mortality counts in administrative regions. A major methodological challenge lies in building realistic models for space-time interactions on discrete irregular spatial graphs. In this paper, we will discuss an observation-driven approach, where past observed counts in neighbouring areas enter directly as explanatory variables, in contrast to the parameter-driven approach through latent Gaussian Markov random fields (Rue and Held, 2005) with spatio-temporal structure. The main focus will lie on the demonstration of the spread of influenza in Germany, obtained through the design and simulation of a spatial extension of the classical SIR model (Hufnagel et al., 2004).

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2006
Deposited On:15 Dec 2010 13:54
Last Modified:05 Apr 2016 14:17
Publisher:Österreichische Statistische Gesellschaft
ISSN:1026-597X
Additional Information:Special Issue on the ROeS-Seminar 2005. Proceedings of the ROeS-Seminar 2005 in Graz, Austria, September 25-29, 2005
Official URL:http://www.stat.tugraz.at/AJS/ausg061/061Held.pdf

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