This research project aims to develop novel statistical methodology for Both retro- and prospective analysis of space-time data on infectious disease incidence. The new techniques will be applied in the particular context of space-time surveillance data, but important parts of the methodology can be used in a wider context. The first sub-project is concerned with appropriate integration of network data in statistical models to better describe the spatio-temporal spread of infectious diseases. Both parameter- and observation-driven models will be suitably extended and statistical algorithms will be developed to incorporate the impact of network data in the analysis.The second sub-project is concerned with the prospective analysis of space-time count data. Statistical algorithms for sequential analysis of time series models will be extended to the space-time setting with particular focus on multivariate outbreak detection. Validation of both retro- and prospective analyses will be based on the assessment of out-of-sample predictions based on proper scoring rules.
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