Flexible Bayesian Hierarchical Models for Estimating Inhalation Exposures

用于估计吸入暴露的灵活贝叶斯分层模型

• 批准号: 10060746
• 负责人: Sudipto Banerjee
• 金额: $ 37.12万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-15 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10060746
• 关键词: Algorithms; Automobile Driving; Bayesian Analysis; Bayesian Modeling; Chemicals; Code; Communities; Computational algorithm; Computer software; Computers; Computing Methodologies; Concentration measurement; Controlled Environment; Data; Data Set; Databases; Decision Analysis; Development; Effectiveness; Emu species; Environmental Health; Equation; Exposure to; Future; Generations; Health Professional; Hybrids; Ice; Inhalation Exposure; Judgment; Knowledge; Laboratories; Markov chain Monte Carlo methodology; Measurement; Measures; Methodology; Methods; Modeling; Monitor; Pattern; Pear; Positioning Attribute; Process; Public Health; Research; Research Personnel; Risk Assessment; Risk Management; Sampling; Scientific Advances and Accomplishments; Scientist; Source; Statistical Algorithm; Statistical Methods; Statistical Models; Statistical sensitivity; Surface; Uncertainty; Validation; Walking; Workplace; air sampling; base; computer generated; design; experimental study; flexibility; improved; innovation; molecular dynamics; operation; particle; physical model; physical process; programs; response; semiparametric; simulation; theories; tool; user friendly software; user-friendly; ventilation

Project Summary/Abstract We propose to develop innovative statistical tools for melding exposure models and observational data aris- ing from measurements of concentrations in controlled chamber conditions. As a first step, we will construct a rich dataset of exposure scenarios in laboratory exposure chambers and real workplace settings, contain- ing data on exposure determinants such as contaminant generation and ventilation rates and exposure mea- surements. We will develop a comprehensive and computationally feasible Bayesian statistical framework for melding the physical exposure models with experimental data from the workplace to effectively account for the sources of uncertainty and produce reliable statistical inference (estimation and predictions). We will employ a Bayesian framework to validate physical models from monitoring data. Our framework will also include formal statistical measures for validating models with observed field data. We do so by assessing how adequately the models capture features and patterns in the monitoring data, applying sensitivity analysis to the choice of priors, and choosing or selecting a model among a set of competing models. We will also develop and disseminate a user-friendly statistical software package that will enable researchers to implement the proposed methods for a wide variety of physical models to analyze their data in a seamless and convenient manner. Upon successful completion of the project, our developments will allow researchers and exposure managers to systematically evaluate retrospective exposure, to predict current and future exposure in the absence of the working process or operation, and to estimate exposure with only a small number of air samples with possibly high variability. With only a few monitoring data points, our Bayesian melding framework will provide more precise estimates of exposure than monitoring. With advances in computational methods and inexpensive software implementation, we purport to exalt formal modeling to an indispensable position in the exposure assessors' armory.

项目摘要/摘要 我们建议开发创新的统计工具，将暴露模型和观测数据ARIS- 来自受控室条件下的浓度测量。作为fi的第一步，我们将构建 实验室曝光室和真实工作场所环境中暴露情景的丰富数据集包含- 关于暴露决定因素的数据，如污染物产生和通风率以及暴露手段- 保证金。我们将开发一个全面的、在计算上可行的贝叶斯统计框架 将物理暴露模型与工作场所的实验数据相结合，以有效地解释 不确定的来源，并产生可靠的统计推断(估计和预测)。我们将聘请一名 根据监测数据验证物理模型的贝叶斯框架。我们的框架还将包括正式的 用观测到的fi数据验证模型的统计方法。我们要做到这一点，需要评估 模型捕捉监测数据中的特征和模式，将敏感度分析应用于先验选择， 以及从一组竞争模型中选择或选择一个模型。我们还将制定和传播一项 用户友好的统计软件包，将使研究人员能够实施建议的方法 各种各样的物理模型，以无缝和方便的方式分析其数据。成功后 项目完成后，我们的开发将使研究人员和曝光管理人员能够系统地 评估回溯性暴露，在没有工作过程的情况下预测当前和未来的暴露 或操作，并估计只有少量可能具有高变异性的空气样本的暴露。 只有几个监测数据点，我们的贝叶斯融合框架将提供更准确的估计 暴露而不是监测。随着计算方法的进步和廉价的软件实现， 我们声称要将正式的建模提升到暴露评估员的武器库中不可或缺的位置。