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A spatially discrete approximation to log-Gaussian Cox processes for modelling aggregated disease count data.
Olatunji Johnson1, Peter Diggle1, Emanuele Giorgi1
1CHICAS, Lancaster Medical School, Lancaster University, Lancaster, UK.
Statistics in Medicine
|August 28, 2019
Summary
This study introduces an efficient discrete approximation for log-Gaussian Cox process (LGCP) models, improving spatial disease analysis. The method offers reliable risk estimates for continuous spatial processes, overcoming limitations of traditional Markov-based models.
Area of Science:
- Spatial statistics
- Epidemiology
- Computational statistics
Background:
- Log-Gaussian Cox process (LGCP) models are used for spatial disease analysis.
- Traditional Markov-based spatial models are limited by their dependence on specific area partitions.
- Spatially continuous prediction is a key challenge in disease mapping.
Purpose of the Study:
- To develop a computationally efficient discrete approximation to LGCP models.
- To enable spatially continuous prediction in disease mapping.
- To provide reliable disease risk estimates for aggregated and continuous spatial scales.
Main Methods:
- Development of a discrete approximation to LGCP models.
- Simulation study to compare predictive performance.
- Application to primary biliary cirrhosis incidence data.
Main Results:
- The proposed approximation overcomes limitations of Markov-based spatial models.
- The method allows for spatially continuous prediction.
- Reliable disease risk estimates were obtained for both continuous and aggregated scales.
Conclusions:
- The discrete approximation to LGCP is a reliable method for analyzing spatially aggregated disease count data.
- The methodology enhances spatial disease modeling by enabling continuous prediction.
- The approach is implemented in the open-source R package SDALGCP.
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