Log Gaussian Cox processes and spatially aggregated disease incidence data

Ye Li*, Patrick Brown, Dionne C. Gesink, Håvard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


This article presents a methodology for modeling aggregated disease incidence data with the spatially continuous log-Gaussian Cox process. Statistical models for spatially aggregated disease incidence data usually assign the same relative risk to all individuals in the same reporting region (census areas or postal regions). A further assumption that the relative risks in two regions are independent given their neighbor's risks (the Markov assumption) makes the commonly used Besag-York-Mollié model computationally simple. The continuous model proposed here uses a data augmentation step to sample from the posterior distribution of the exact locations at each step of an Markov chain Monte Carlo algorithm, and models the exact locations with an log-Gaussian Cox process. A simulation study shows the log-Gaussian Cox process model consistently outperforming the Besag-York-Mollié model. The method is illustrated by making inference on the spatial distribution of syphilis risk in North Carolina. The effect of several known social risk factors are estimated, and areas with risk well in excess of that expected given these risk factors are identified.

Original languageEnglish (US)
Pages (from-to)479-507
Number of pages29
JournalStatistical Methods in Medical Research
Issue number5
StatePublished - Oct 2012
Externally publishedYes


  • Geostatistics
  • Markov random field
  • data augmentation
  • disease mapping

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management


Dive into the research topics of 'Log Gaussian Cox processes and spatially aggregated disease incidence data'. Together they form a unique fingerprint.

Cite this