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    Rights statement: This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 35, 2019 DOI: 10.1016/j.spasta.2019.100397

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Geostatistical methods for modelling non-stationary patterns in disease risk

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Geostatistical methods for modelling non-stationary patterns in disease risk. / Ejigu, Bedilu A.; Wencheko, Eshetu; Moraga-Serrano, Paula; Giorgi, Emanuele.

In: Spatial Statistics, Vol. 35, 100397, 09.12.2019.

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Ejigu, Bedilu A. ; Wencheko, Eshetu ; Moraga-Serrano, Paula ; Giorgi, Emanuele. / Geostatistical methods for modelling non-stationary patterns in disease risk. In: Spatial Statistics. 2019 ; Vol. 35.

Bibtex

@article{601f29f3e7ed4260892f83fe81b97c65,
title = "Geostatistical methods for modelling non-stationary patterns in disease risk",
abstract = "One of the tenets of geostatistical modelling is that close things in space are more similar than distant things, a principle also known as “the first law of geography”. However, this may be questionable when unmeasured covariates affect, not only the mean of the underlying process, but also its covariance structure. In this paper we go beyond the assumption of stationarity and propose a novel modelling approach which we justify in the context of disease mapping. More specifically, our goal is to incorporate spatially referenced risk factors into the covariance function in order to model non-stationary patterns in the health outcome under investigation. Through a simulation study, we show that ignoring such non-stationary effects can lead to invalid inferences, yielding prediction intervals whose coverage is well below the nominal confidence level. We then illustrate two applications of the developed methodology for modelling anaemia in Ethiopia and Loa loa risk in West Africa. Our results indicate that the non-stationary models give a better fit than standard geostatistical models yielding a lower value for the Akaike information criterion. In the last section, we conclude by discussing further extensions of the new methods.",
keywords = "Disease mapping, Gaussian process, Model-based geostatistics, Stationarity",
author = "Ejigu, {Bedilu A.} and Eshetu Wencheko and Paula Moraga-Serrano and Emanuele Giorgi",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 35, 2019 DOI: 10.1016/j.spasta.2019.100397",
year = "2019",
month = dec,
day = "9",
doi = "10.1016/j.spasta.2019.100397",
language = "English",
volume = "35",
journal = "Spatial Statistics",
issn = "2211-6753",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Geostatistical methods for modelling non-stationary patterns in disease risk

AU - Ejigu, Bedilu A.

AU - Wencheko, Eshetu

AU - Moraga-Serrano, Paula

AU - Giorgi, Emanuele

N1 - This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 35, 2019 DOI: 10.1016/j.spasta.2019.100397

PY - 2019/12/9

Y1 - 2019/12/9

N2 - One of the tenets of geostatistical modelling is that close things in space are more similar than distant things, a principle also known as “the first law of geography”. However, this may be questionable when unmeasured covariates affect, not only the mean of the underlying process, but also its covariance structure. In this paper we go beyond the assumption of stationarity and propose a novel modelling approach which we justify in the context of disease mapping. More specifically, our goal is to incorporate spatially referenced risk factors into the covariance function in order to model non-stationary patterns in the health outcome under investigation. Through a simulation study, we show that ignoring such non-stationary effects can lead to invalid inferences, yielding prediction intervals whose coverage is well below the nominal confidence level. We then illustrate two applications of the developed methodology for modelling anaemia in Ethiopia and Loa loa risk in West Africa. Our results indicate that the non-stationary models give a better fit than standard geostatistical models yielding a lower value for the Akaike information criterion. In the last section, we conclude by discussing further extensions of the new methods.

AB - One of the tenets of geostatistical modelling is that close things in space are more similar than distant things, a principle also known as “the first law of geography”. However, this may be questionable when unmeasured covariates affect, not only the mean of the underlying process, but also its covariance structure. In this paper we go beyond the assumption of stationarity and propose a novel modelling approach which we justify in the context of disease mapping. More specifically, our goal is to incorporate spatially referenced risk factors into the covariance function in order to model non-stationary patterns in the health outcome under investigation. Through a simulation study, we show that ignoring such non-stationary effects can lead to invalid inferences, yielding prediction intervals whose coverage is well below the nominal confidence level. We then illustrate two applications of the developed methodology for modelling anaemia in Ethiopia and Loa loa risk in West Africa. Our results indicate that the non-stationary models give a better fit than standard geostatistical models yielding a lower value for the Akaike information criterion. In the last section, we conclude by discussing further extensions of the new methods.

KW - Disease mapping

KW - Gaussian process

KW - Model-based geostatistics

KW - Stationarity

U2 - 10.1016/j.spasta.2019.100397

DO - 10.1016/j.spasta.2019.100397

M3 - Journal article

VL - 35

JO - Spatial Statistics

JF - Spatial Statistics

SN - 2211-6753

M1 - 100397

ER -