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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要我们建议开发创新的统计工具来融合暴露模型和观测数据作为第一步，我们将根据受控室条件下的浓度测量来构建。实验室暴露室和真实工作场所环境中暴露场景的丰富数据集，包含- 荷兰国际集团关于暴露决定因素的数据，例如污染物的产生和通风率以及暴露测量我们将开发一个全面且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性的来源并产生可靠的统计推断（估计和预测）。用于根据监测数据验证物理模型的贝叶斯框架还将包括正式的框架。我们通过评估模型的充分性来实现这一点。模型捕获监测数据中的特征和模式，将敏感性分析应用于先验的选择，我们还将开发并传播一个模型。用户友好的统计软件包将使研究人员能够实施所提出的方法成功后，可以使用各种物理模型以无缝且方便的方式分析数据。项目完成后，我们的开发将使研究人员和暴露管理人员能够系统地评估回顾性暴露，在没有工作流程的情况下预测当前和未来的暴露或操作，并仅使用少量可能具有高变异性的空气样本来估计暴露情况。只需几个监测数据点，我们的贝叶斯融合框架将提供更精确的估计随着计算方法的进步和廉价的软件实施，我们旨在将形式建模提升到暴露评估者武器库中不可或缺的地位。