The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when data is arising from a variety of sources, not necessarily at the same spatial scale, it is possible to specify spatial random effects, or covariate effects, at the areal level, by using a multiple membership principle. In this talk I will investigate the theoretical underpinnings of these application of the multiple membership principle to the CAR prior, in particular with regard to parameterisation, properness and identifiability, and I will  present the results of an application of the multiple membership model to diabetes prevalence data in South London, together with strategic implications for public health considerations.

Silvia Liverani is Reader in Statistics, and Head of the Research Centre in Probability, Statistics and Data Science in the School of Mathematical Sciences at Queen Mary University of London, UK. Her research interests are in Bayesian statistics, and in particular Dirichlet proces mixture models, clustering and spatio-temporal modelling, with application in epidemiology and biodiversity.