Spatio-Temporal Infectious Disease Epidemiology based on Point Processes
نویسندگان
چکیده
In this Master’s Thesis, a novel combination of point process models continuous in space-time is proposed for infectious disease data. Modelling is driven by the conditional intensity function, which enables a step towards a regression framework for self-exciting spatio-temporal point processes. The model is an extension of the discrete space additive-multiplicative conditional intensity model proposed by Höhle (2009a), and also borrows from earthquake research, especially from the formulation of the space-time ETAS model in Ogata, Katsura & Tanemura (2003). Estimation is performed by means of full maximum likelihood, which for general point processes in space requires the evaluation of two-dimensional integrals. Therefore, various methods of numerical integration are investigated as a prerequisite for maximum likelihood inference. The particular application of interest is the stochastic modelling of the transmission dynamics of the two most common meningococcal strains observed in Germany in 2002–2008. The application showed that the proposed model and its estimation by the provided thoroughly R implementation are applicable and valuable for the analysis of spatio-temporal infectious disease data.
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تاریخ انتشار 2010