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Dargatz, Christiane
Georgescu, Vera
Held, Leonhard
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Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 450
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 & 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).
Space-time interaction
Gaussian Markov random fields
Epidemic modelling
Stochastic differential equations
Global SIR model
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Working Paper

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