The root-Gaussian Cox Process for spatial-temporal disease mapping with aggregated data

Zeytu Gashaw Asfaw (), Patrick E. Brown and Jamie Stafford
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Zeytu Gashaw Asfaw: Addis Ababa University
Patrick E. Brown: University of Toronto
Jamie Stafford: University of Toronto

Computational Statistics, 2025, vol. 40, issue 3, No 2, 1184 pages

Abstract: Abstract The study of aggregated data influenced by time, space, and extra changes in geographic region borders was the main emphasis of the current paper. This may occur if the regions used to count the reported incidences of a health outcome over time change periodically. In order to handle the spatial-temporal scenario, we enhance the spatial root-Gaussian Cox Process (RGCP), which makes use of the square-root link function rather than the more typical log-link function. The algorithm’s ability to estimate a risk surface has been proven by a simulation study, and it has also been validated by real datasets.

Keywords: EMS algorithm; Spatial statistics; Gaussian random field; Matérn correlation; AR(1) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01532-y

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