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

TL;DR

This paper discusses spatial modeling techniques for disease prevalence mapping in low-resource settings, emphasizing the importance of accounting for complex survey designs and spatial dependencies to improve policy guidance.

Contribution

It introduces a model-based approach using spatial Gaussian random fields to better handle clustering in complex survey data.

Findings

Model-based spatial approach improves prevalence estimates

Acknowledges clustering in survey data

Enhances policy-relevant disease mapping

Abstract

Prevalence mapping in low resource settings is an increasingly important endeavor to guide policy making and to spatially and temporally characterize the burden of disease. We will focus our discussion on consideration of the complex design when analyzing survey data, and on spatial modeling. With respect to the former, we consider two approaches: direct use of the weights, and a model-based approach using a spatial model to acknowledge clustering. For the latter we consider continuously indexed Markovian Gaussian random field models.