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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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    Embargo ends: 9/12/20

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Geostatistical methods for modelling non-stationary patterns in disease risk

Research output: Contribution to journalJournal article

E-pub ahead of print
Article number100397
<mark>Journal publication date</mark>9/12/2019
<mark>Journal</mark>Spatial Statistics
Volume35
Number of pages15
Publication StatusE-pub ahead of print
Early online date9/12/19
<mark>Original language</mark>English

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.

Bibliographic note

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