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    Rights statement: This is the peer reviewed version of the following article: Giorgi, E., Diggle, P. J., Snow, R. W., and Noor, A. M. (2018) Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys. International Statistical Review, 86: 571–597. https://doi.org/10.1111/insr.12268.which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/insr.12268/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

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Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys

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Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys. / Giorgi, Emanuele; Diggle, Peter John; Snow, Robert W.; Noor, Abdisalan M.

In: International Statistical Review, Vol. 86, No. 3, 12.2018, p. 571-597.

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Giorgi, Emanuele ; Diggle, Peter John ; Snow, Robert W. ; Noor, Abdisalan M. / Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys. In: International Statistical Review. 2018 ; Vol. 86, No. 3. pp. 571-597.

Bibtex

@article{8d4ff142548448a88ab35cb39ca30e18,
title = "Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys",
abstract = "In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram‐based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood‐based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatio‐temporal covariance function is not supported by the data. We discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates. We describe statistical software for the visualisation of spatio‐temporal predictive summaries of prevalence through interactive animations. Finally, we illustrate an application to historical malaria prevalence data from 1 334 surveys conducted in Senegal between 1905 and 2014.",
author = "Emanuele Giorgi and Diggle, {Peter John} and Snow, {Robert W.} and Noor, {Abdisalan M.}",
note = "This is the peer reviewed version of the following article: Giorgi, E., Diggle, P. J., Snow, R. W., and Noor, A. M. (2018) Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys. International Statistical Review, 86: 571–597. https://doi.org/10.1111/insr.12268.which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/insr.12268/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.",
year = "2018",
month = dec,
doi = "10.1111/insr.12268",
language = "English",
volume = "86",
pages = "571--597",
journal = "International Statistical Review",
issn = "0306-7734",
publisher = "International Statistical Institute",
number = "3",

}

RIS

TY - JOUR

T1 - Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys

AU - Giorgi, Emanuele

AU - Diggle, Peter John

AU - Snow, Robert W.

AU - Noor, Abdisalan M.

N1 - This is the peer reviewed version of the following article: Giorgi, E., Diggle, P. J., Snow, R. W., and Noor, A. M. (2018) Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys. International Statistical Review, 86: 571–597. https://doi.org/10.1111/insr.12268.which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/insr.12268/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2018/12

Y1 - 2018/12

N2 - In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram‐based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood‐based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatio‐temporal covariance function is not supported by the data. We discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates. We describe statistical software for the visualisation of spatio‐temporal predictive summaries of prevalence through interactive animations. Finally, we illustrate an application to historical malaria prevalence data from 1 334 surveys conducted in Senegal between 1905 and 2014.

AB - In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram‐based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood‐based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatio‐temporal covariance function is not supported by the data. We discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates. We describe statistical software for the visualisation of spatio‐temporal predictive summaries of prevalence through interactive animations. Finally, we illustrate an application to historical malaria prevalence data from 1 334 surveys conducted in Senegal between 1905 and 2014.

U2 - 10.1111/insr.12268

DO - 10.1111/insr.12268

M3 - Journal article

VL - 86

SP - 571

EP - 597

JO - International Statistical Review

JF - International Statistical Review

SN - 0306-7734

IS - 3

ER -