Modeling Geo-Located Public Health Data Using Spatio-Temporal Log-Gaussian Cox Processes

• Activity Number: 174
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Theresa Smith* and Peter J. Diggle and Ben Taylor
• Companies: Lancaster University and Lancaster University and Lancaster University
• Keywords: spatio-temporal ; geostatistics ; epidemiology ; Cox process ; spatial point process

Abstract

We present a spatio-temporal log-Gaussian Cox process (LGCP) and a Bayesian approach to joint estimation of the latent Gaussian process (GP) and the effects of environmental predictors on the spatio-temporal intensity surface. LGCPs are a type of inhomogeneous Poisson point process where the log intensity surface is a GP. A point process approach is useful when each observation is indexed to a particular point in space and time. This is in contrast to the common area-level approach in epidemiology wherein observations and risk factors are summarised over several small regions (e.g., counties or local authorities). The spatially-continuous approach inherent in LGCPs naturally accommodates risk factors measured on different spatio-temporal units and avoids some forms of ecological bias. We compare maximum likelihood and Bayesian techniques for estimating systematic trends in the spatio-temporal risk surface as well as the latent GP. Finally we use a spatio-temporal LGCP to investigate the roles of environmental and socio-economic risk-factors in the incidence of campylobacter (a common bacterial case of food borne disease) in the UK.