PhD: A Spatially discrete approximation to log-Gaussian Cox processes for modelling aggregated disease count data
- A Spatially discrete approximation to log-Gaussian Cox processes for modelling aggregated disease count data
This paper is a part of a single PhD “Geostatistical Methods for Modelling Spatially Aggregated Data”
In this project, we develop a computationally efficient discrete approximation to log-Gaussian Cox process (LGCP) models for the analysis of spatially aggregated disease count data.
Our approach overcomes an inherent limitation of spatial models based on Markov structures, namely, that each such model is tied to a specific partition of the study area, and allows for spatially continuous prediction.
We compare the predictive performance of our modelling approach with LGCP through a simulation study and an application to primary biliary cirrhosis incidence data in Newcastle upon Tyne, UK.
Our results suggest that, when disease risk is assumed to be a spatially continuous process, the proposed approximation to LGCP provides reliable estimates of disease risk both on spatially continuous and aggregated scales.
The proposed methodology is implemented in the open-source R package SDALGCP.
Keywords: disease mapping, geostatistics, log-Gaussian Cox process, Monte Carlo maximum likelihood