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
Accepted author manuscript, 1.1 MB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -