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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

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

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Model-based geostatistics for prevalence mapping in low-resource settings. / Diggle, Peter John; Giorgi, Emanuele.

In: Journal of the American Statistical Association, Vol. 111, No. 515, 10.2016, p. 1096-1120.

Research output: Contribution to journalJournal article

Harvard

Diggle, PJ & Giorgi, E 2016, 'Model-based geostatistics for prevalence mapping in low-resource settings', Journal of the American Statistical Association, vol. 111, no. 515, pp. 1096-1120. https://doi.org/10.1080/01621459.2015.1123158

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Diggle, Peter John ; Giorgi, Emanuele. / Model-based geostatistics for prevalence mapping in low-resource settings. In: Journal of the American Statistical Association. 2016 ; Vol. 111, No. 515. pp. 1096-1120.

Bibtex

@article{1f39bf77b5894104a4b4fd6f6104ad6e,
title = "Model-based geostatistics for prevalence mapping in low-resource settings",
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.",
keywords = "Geostatistics, multiple surveys, prevalence, spatio-temporal models, zero-inflation",
author = "Diggle, {Peter John} and Emanuele Giorgi",
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",
year = "2016",
month = oct
doi = "10.1080/01621459.2015.1123158",
language = "English",
volume = "111",
pages = "1096--1120",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "515",

}

RIS

TY - JOUR

T1 - Model-based geostatistics for prevalence mapping in low-resource settings

AU - Diggle, Peter John

AU - Giorgi, Emanuele

N1 - 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

PY - 2016/10

Y1 - 2016/10

N2 - 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.

AB - 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.

KW - Geostatistics

KW - multiple surveys

KW - prevalence

KW - spatio-temporal models

KW - zero-inflation

U2 - 10.1080/01621459.2015.1123158

DO - 10.1080/01621459.2015.1123158

M3 - Journal article

VL - 111

SP - 1096

EP - 1120

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 515

ER -