Bayesian Modeling and Inference for High-Dimensional Disease Mapping and Boundary Detection"

Project Summary/Abstract This application seeks to advance statistical methods within the Bayesian inferential paradigm for disease map- ping and spatial boundary analysis. Disease mapping is an epidemiological technique used to describe the geographic variation of disease and to generate etiological hypotheses about the possible causes for apparent differences in risk. The last decade has seen an explosion of interest in disease mapping, with recent method- ological developments in advanced spatial statistics and increasing availability of computerized Geographic In- formation Systems (GIS) technology. Spatial biostatisticians, data scientists and epidemiologists today routinely encounter datasets requiring multi- or high-dimensional disease mapping in the presence of spatial-temporal misalignment, where “dimension” refers to (a) the number of cancer types being studied, (b) the number of spa- tial units (e.g., census-tracts, counties) in the map, and (c) the number of temporal units (time points) at which the data are observed. This application offers novel classes of stochastic process-based graphical models with specific attention to spatially-temporally misaligned data and modeling of multiple cancers. The versatility and scalability of the proposed framework will allow epidemiologists and public health researchers to account for information from multiple sources including, but not limited to, environmental factors and climate-related vari- ables at arbitrary resolutions in spatial-temporal “BIG DATA” settings. The proposal will subsequently develop a rigorous framework for multivariate boundary detection on maps, where boundaries delineate regions with significantly different spatial effects.