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.

项目摘要/摘要 该应用程序旨在推进贝叶斯推论范式中疾病图的统计方法 - ping和空间边界分析。疾病映射是一种流行病学技术，用于描述 疾病的地理差异并产生有关外观原因的病因假设 风险差异。过去十年中，人们对疾病制图的兴趣激增，最近的方法 - 高级空间统计数据中的科学发展以及计算机地理的可用性增加 编队系统（GIS）技术。当今的空间生物统计学家，数据科学家和流行病学家常规 在存在时空存在下需要多维疾病映射的遇到数据集 未对准，“维度”是指（a）研究癌症类型的数量，（b）SPA-的数量 地图中的TIAL单位（例如，人口普查，县），以及（c）临时单位（时间点）的数量 观察到数据。该应用程序提供了新颖的基于随机过程的图形模型， 特别关注多种癌症的空间临时数据和模型。多功能性和 拟议框架的可伸缩性将使流行病学家和公共卫生研究人员可以考虑 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化 在时空“大数据”设置中，有可能在任意决议中。该提议随后将发展 在地图上进行多元边界检测的严格框架，边界描绘了带有的区域 显着不同的空间效应。