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

用于高维疾病绘图和边界检测的贝叶斯建模和推理”

• 批准号: 10568797
• 负责人: Sudipto Banerjee
• 金额: $ 29.37万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10568797
• 关键词: Address; Algorithms; Area; Atlas of Cancer Mortality in the United States; Attention; Bayesian Analysis; Bayesian Method; Bayesian Modeling; Bayesian learning; Censuses; Climate; Collection; Complex; Computer software; Computing Methodologies; County; Data; Data Scientist; Data Set; Databases; Dependence; Detection; Development; Dimensions; Disease; Disparate; Endowment; Environment; Environmental Pollutants; Environmental Risk Factor; Epidemiologic Methods; Epidemiologist; Epidemiology; Etiology; Exercise; Explosion; Funding; Geographic Information Systems; Geography; Graph; Health; Incidence; Internet; Learning; Link; Malignant Neoplasms; Maps; Markov chain Monte Carlo methodology; Methodology; Methods; Modeling; Mortality Map; National Cancer Institute; Outcome; Prevalence; Process; Public Health; Publishing; Research Personnel; Resolution; Risk; Sample Size; Source; Statistical Computing; Statistical Methods; Statistical Models; Stochastic Processes; Technology; Time; cancer type; computerized; geographic difference; graphical user interface; high dimensionality; innovation; interest; mortality; novel; sociodemographic factors; socioeconomics; spatiotemporal; statistics; technology platform; trend; user friendly software; web interface

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.

项目概要/摘要 该应用程序旨在推进疾病图谱的贝叶斯推理范式内的统计方法 - ping 和空间边界分析是一种用于描述疾病的流行病学技术。 疾病的地理变异并产生关于明显的可能原因的病因学假设 过去十年，人们对疾病绘图的兴趣激增，最近的方法是： 先进空间统计的发展和计算机化地理信息的可用性不断提高 形成系统（GIS）技术是当今空间生物统计学家、数据科学家和流行病学家的日常工作。 在存在时空的情况下遇到需要多维或高维疾病映射的数据集 错位，其中“维度”是指（a）正在研究的癌症类型的数量，（b）spa-的数量 地图中的基本单位（例如人口普查区、县），以及 (c) 时间单位（时间点）的数量 该应用程序提供了基于随机过程的新型图形模型。 特别关注时空错位的数据和多种癌症的建模。 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解释 来自多个来源的信息，包括但不限于环境因素和气候相关变量 该提案随后将在时空“大数据”环境中以任意分辨率进行开发。 地图上多元边界检测的严格框架，其中边界用 空间效果差异显着。