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

Author

Listed:
  • Zeytu Gashaw Asfaw

    (Addis Ababa University)

  • Patrick E. Brown

    (University of Toronto
    Center for Global Health Research)

  • Jamie Stafford

    (University of Toronto)

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.

Suggested Citation

  • Zeytu Gashaw Asfaw & Patrick E. Brown & Jamie Stafford, 2025. "The root-Gaussian Cox Process for spatial-temporal disease mapping with aggregated data," Computational Statistics, Springer, vol. 40(3), pages 1171-1184, March.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01532-y
    DOI: 10.1007/s00180-024-01532-y
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    References listed on IDEAS

    as
    1. Brown, Patrick E., 2015. "Model-Based Geostatistics the Easy Way," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i12).
    2. P. Nguyen & P. E. Brown & J. Stafford, 2012. "Mapping Cancer Risk in Southwestern Ontario with Changing Census Boundaries," Biometrics, The International Biometric Society, vol. 68(4), pages 1228-1237, December.
    3. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
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