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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.

项目总结/文摘

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

Spatial factor modeling: A Bayesian matrix-normal approach for misaligned data.