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

项目摘要/摘要 这项应用寻求在贝叶斯推断范例内推进疾病地图的统计方法- PING和空间边界分析。疾病图谱是一种流行病学技术，用于描述 疾病的地理变异并产生关于明显疾病可能原因的病因学假说 风险差异。在过去的十年里，人们对疾病地图的兴趣激增，最近的方法-- 高级空间统计学的科学发展和计算机地理在中国的应用 队形系统(GIS)技术。如今，空间生物统计学家、数据科学家和流行病学家 遇到需要在存在时空的情况下绘制多维或高维疾病地图的数据集 错位，其中“维度”是指(A)正在研究的癌症类型的数量，(B)spa-spa的数量- 地图中的时间单位(例如，人口普查区、县)，以及(C)时间单位(时间点)的数量 观察了数据。该应用程序提供了基于随机过程的图形模型的新类别，具有 Speciific注意时空错位的数据和对多种癌症的建模。多功能性和 拟议框架的可扩展性将使流行病学家和公共卫生研究人员能够说明 来自多个来源的信息，包括但不限于环境因素和与气候有关的变量- 能够在时空“大数据”环境中以任意分辨率工作。该提案将在随后制定 地图上多变量边界检测的严格框架，其中边界用 fi有明显不同的空间效应。

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.