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Prevalence mapping in low resource settings is an increasingly importantendeavor to guide policy making and to spatially and temporally characterizethe burden of disease. We will focus our discussion on consideration of thecomplex design when analyzing survey data, and on spatial modeling. Withrespect to the former, we consider two approaches: direct use of the weights,and a model-based approach using a spatial model to acknowledge clustering. Forthe latter we consider continuously indexed Markovian Gaussian random fieldmodels.

Spatial Modeling, with Application to Complex Survey Data: Discussion of  "Model-based Geostatistics for Prevalence Mapping in Low-Resource Settings",  by Diggle and Giorgi