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：
10295781
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 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10295781
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.

项目摘要/摘要我们建议开发创新的统计工具，以融合暴露模型和观察数据。从受控腔室条件下浓度的测量。作为第一个步骤，我们将构建实验室暴露室和实际工作场所环境中的暴露场景的丰富数据集包含 - 关于暴露的数据决定了污染物产生和通风率以及暴露率等保证。我们将为一个全面且计算上可行的贝叶斯统计框架将身体暴露模型与来自工作场所的实验数据融合在一起，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）。我们将采用一个贝叶斯框架以从监视数据中验证物理模型。我们的框架也将包括正式使用观察到的字段数据验证模型的统计措施。我们通过评估了如何适当的模型在监视数据中捕获特征和模式，将灵敏度分析应用于先验的选择，并在一组竞争模型中选择或选择模型。我们还将发展并传播用户友好的统计软件软件包，该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且方便的方式分析其数据。成功完成项目的完成，我们的发展将使研究人员和接触经理系统地进行评估回顾性暴露，以预测在没有工作过程的情况下当前和将来的暴露或操作，并仅使用少量的空气样本估算暴露，可能会有很高的可变性。只有几个监视数据点，我们的贝叶斯融合框架将提供更精确的估计。暴露于监视。随着计算方法和廉价软件实施的进步，我们声称将正式的建模升至暴露评估者的军械库中必不可少的位置。

项目成果

期刊论文数量（21）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models.

DOI：
10.1214/19-ba1177
发表时间：
2019-12
期刊：
Bayesian analysis
影响因子：
4.4
作者：
Datta A;Banerjee S;Hodges JS;Gao L
通讯作者：
Gao L

Assessing Exposures from the Deepwater Horizon Oil Spill Response and Clean-up.

评估深水地平线溢油响应和清理的暴露。

DOI：
10.1093/annweh/wxab107
发表时间：
2022
期刊：
Annals of work exposures and health
影响因子：
2.6
作者：
Stewart,Patricia;Groth,CarolineP;Huynh,TranB;GormanNg,Melanie;Pratt,GregoryC;Arnold,SusanF;Ramachandran,Gurumurthy;Banerjee,Sudipto;Cherrie,JohnW;Christenbury,Kate;Kwok,RichardK;Blair,Aaron;Engel,LawrenceS;Sandler,DaleP
通讯作者：
Sandler,DaleP

Bayesian Spatial Modeling for Housing Data in South Africa.