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 including river-blindness mapping Africa-wide.
Diggle, P.J. and Giorgi, E. (2016). Model-based geostatistics for prevalence Mapping in low-resource settings (with Discussion). Journal of the American Statistical Association (to appear).