Thursday 25 Feb 2016Statistical Science Seminar: Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings

Peter Diggle - Lancaster University

H101 14:30-16:30

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).

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