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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 02/02/2016, available online: http://www.tandfonline.com/10.1080/01621459.2015.1123158

    Accepted author manuscript, 4.61 MB, PDF document

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Model-based geostatistics for prevalence mapping in low-resource settings

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>10/2016
<mark>Journal</mark>Journal of the American Statistical Association
Issue number515
Volume111
Number of pages25
Pages (from-to)1096-1120
Publication statusPublished
Early online date2/02/16
Original languageEnglish

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.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 02/02/2016, available online: http://www.tandfonline.com/10.1080/01621459.2015.1123158