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

TL;DR

This paper reviews and extends geostatistical models for disease prevalence mapping in low-resource settings, incorporating various data sources and methodological enhancements to improve accuracy and applicability.

Contribution

It introduces new methodological extensions for combining different survey data types, spatio-temporal modeling, and zero-inflation handling tailored for disease mapping in resource-limited environments.

Findings

Enhanced methods for integrating biased and unbiased survey data

Development of spatio-temporal disease mapping models

Addressed zero-inflation in prevalence data

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 binomial error distribution, logistic link and a combination of explanatory variables and a Gaussian spatial stochastic process in the linear predictor. In this paper, we 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: methods for combining randomised survey data with data from non-randomised, and therefore potentially biased, surveys; spatio-temporal extensions; spatially structured zero-inflation. Throughout, we illustrate the methods with disease mapping applications that have arisen through our involvement with a range of African public health programmes.