Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings

• Activity Number: 102
• Type: Invited
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: ASA
• Author(s): Peter J. Diggle* and Emanuele Giorgi
• Companies: Lancaster University and Lancaster University

Abstract

In low-resource settings, prevalence mapping relies on empirical prevalence data from a finite, often spatially sparse, set of surveys of communities within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model with logistic link, binomial error distribution and a Gaussian spatial process as a stochastic component of the linear predictor.

In this talk, I will first review statistical methods and software associated with this standard model, then consider several methodological extensions 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-in flation. Finally, I will also describe disease mapping applications that have arisen through collaboration with a range of African public health programmes.