Bayesian Modeling for Physical Processes in Industrial Hygiene Using Misaligned Workplace Data

Details

• Personal Author:
  Banerjee S ; Monteiro JVD ; Ramachandran G ; Banerjee S ; Monteiro JVD ; Ramachandran G
• Description:
  In industrial hygiene, a worker's exposure to chemical, physical, and biological agents is increasingly being modeled using deterministic physical models that study exposures near and farther away from a contaminant source. However, predicting exposure in the workplace is challenging and simply regressing on a physical model may prove ineffective due to biases and extraneous variability. A further complication is that data from the workplace are usually misaligned. This means that not all timepoints measure concentrations near and far from the source. We recognize these challenges and outline a flexible Bayesian hierarchical framework to synthesize the physical model with the field data. We reckon that the physical model, by itself, is inadequate for enhanced inferential and predictive performance and deploy (multivariate) Gaussian processes to capture uncertainties and associations. We propose rich covariance structures for multiple outcomes using latent stochastic processes. This article has supplementary material available online. [Description provided by NIOSH]
• Subjects:
  Occupational Exposure Occupational Health Safety
• Keywords:
  Author Keywords: Bayesian Melding Cross-covariances Exposure Assessment Gaussian Processes Industrial Hygiene Linear Ordinary Differential Equations Markov Chain Monte Carlo Models Occupational Exposure Occupational Exposure Models Statistical Analysis

  More +
• ISSN:
  0040-1706
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article ; Journal Article
• Place as Subject:
  California ; Minnesota ; North Carolina ; OSHA Region 4 ; OSHA Region 5 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH) ; National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health ; Workplace Safety & Health
• Location:
  North America ; North America
• Pages in Document:
  238-247
• Volume:
  56
• Issue:
  2
• NIOSHTIC Number:
  nn:20055686
• Citation:
  Technometrics 2014 Apr; 56(2):238-247
• Contact Point Address:
  Joao V. D. Monterio, Department of Statistical Sciences, Duke University, Durham, NC 27708
• Email:
  m397@stat.duke.edu
• Federal Fiscal Year:
  2014
• Performing Organization:
  University of California, Los Angeles
• Peer Reviewed:
  True
• Start Date:
  20130901
• Source Full Name:
  Technometrics
• End Date:
  20170831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:3f380cc14956c43652f7f33737bcbcc198622906351bebcd926e613175762ce8590add3ac3f33999595fb9b915af80b29f2507c274fe61c3f7dd8a5045655991
• Download URL:
  https://stacks.cdc.gov/view/cdc/213476/cdc_213476_DS1.pdf
• File Type:
  [PDF - 597.65 KB ]