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Continuous inference for aggregated point process data

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If you have a question about this talk, please contact Alison Quenault.

We introduce new methods for inference with count data registered on a set of aggregation units. Such data are omnipresent in epidemiology due to confidentiality issues: it is much more common to know the county in which an individual resides, say, than know their exact location in space. Inference for aggregated data has traditionally made use of models for discrete spatial variation, for example conditional autoregressive models (CAR). We argue that such discrete models can be improved from both a scientific and inferential perspective by using spatiotemporally continuous models to directly model the aggregated counts. We introduce methods for delivering (limiting) continuous inference with spatitemporal aggregated count data in which the aggregation units might change over time and are subject to uncertainty. We illustrate our methods using real world examples and discuss the implementation of the methods on graphics processing units, which yields massive computational benefits.

This talk is part of the MRC Biostatistics Unit Seminars series.

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