B0225
Title: Model-based geostatistics for prevalence mapping in low-resource settings
Authors: Peter Diggle - Lancaster University and University of Liverpool (United Kingdom) [presenting]
Emanuele Giorgi - Lancaster University (United Kingdom)
Abstract: Statistical methods and software associated with the standard model are first reviewed, then several methodological extensions are considered whose development has been motivated by the requirements of specific applications. These include: low-rank approximations for use with large data-sets; methods for combining randomised survey data with data from non-randomised, and therefore potentially biased, surveys; spatio-temporal extensions; spatially structured zero-inflation. Finally, we will also describe disease mapping applications that have arisen through collaboration with a range of African public health programmes.
