PETER DIGGLE (Lancaster University, John Hopkins University)

Model-based Geostatistics for Tropical Disease Epidemiology

Geostatistical methods are relevant when there is scientic interest in the behaviour of a spatially continuous process S(x) which is not directly observable. Instead, spatially discrete data Y_i : i = 1,..., n are available, and Y_i is stochasticaly related to S(x_i).

Problems of this kind arise naturally in tropical disease epidemiology because complete assessment of disease incidence or prevalence in the population of interest (typically one or more developing countries) is infeasible. Instead, spatial variation in incidence or prevalence must be inferred from incomplete sampling of selected communities within the population of interest.

Diggle, Moyeed and Tawn (1998) coined the phrase "model-based geostatistics" to mean the application of general principles of statistical modelling and inference to geostatistical problems.

In this talk, I will review the basic ideas of model-based geostatistics and describe an application to the estimation of spatial variation in the prevalence of Loa loa (river blindness) in Africa, using both univariate (Thompson et al, 2007) and bivariate (Crainiceanu, Diggle and Rowlingson, 2007) models.

References:

• Crainiceanu, C., Diggle, P.J. and Rowlingson, B.S. (2007) Bivariate modelling and prediction of spatial variation in Loa loa prevalence in tropical Africa (with Discussion). Journal of the American Statistical Association (to appear)
• Diggle, P.J., Moyeed, R.A. and Tawn, J.A. (1998). Model-based Geostatistics (with Discussion). Applied Statistics 47 299-350.
• Thomson, M.C., Christensen, O.F., Obsomer, V., Rowlingson, B., Gardon, J., Wani, S., Takougang, I., Enyong, P., Kamgno, J., Remme, H., Boussinesq, M., Molyneux, D.H. and Diggle, P.J. (2007). Spatial modelling and prediction of Loa loa risk: decision making under uncertainty. Annals of Tropical Medicine and Parasitology (to appear).