Bayesian hierarchical modeling and inference for mechanistic systems in industrial hygiene.
A series of experiments in stationary and moving passenger rail cars were conducted to measure removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols and the air changes per hour provided by existing and modified air handling systems. Such methods for exposure assessments are cus...
| Publicado en: | Annals of Work Exposures & Health Vol. 68; no. 8; pp. 834 - 846 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180268291&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180268291 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23987308 KJ1E jtl: Annals of Work Exposures & Health issn: 23987308 maglogo: N pubinfo: dt: Oct2024 vid: 68 iid: 8 pid: 622 pub: Oxford University Press / USA artinfo: ui: 180268291 180268291 180268291 10.1093/annweh/wxae061 180268291 ppf: 834 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Bayesian hierarchical modeling and inference for mechanistic systems in industrial hygiene. aug: au: Pan, Soumyakanti Das, Darpan Ramachandran, Gurumurthy Banerjee, Sudipto affil: Department of Biostatistics, University of California Los Angeles , 650 Charles E. Young Drive South, Los Angeles, CA 90095-1772 , United States sug: subj: Models, Statistical Occupational Health Hygiene Aerosols Human Funding Source Experimental Studies Descriptive Statistics ab: A series of experiments in stationary and moving passenger rail cars were conducted to measure removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols and the air changes per hour provided by existing and modified air handling systems. Such methods for exposure assessments are customarily based on mechanistic models derived from physical laws of particle movement that are deterministic and do not account for measurement errors inherent in data collection. The resulting analysis compromises on reliably learning about mechanistic factors such as ventilation rates, aerosol generation rates, and filtration efficiencies from field measurements. This manuscript develops a Bayesian state-space modeling framework that synthesizes information from the mechanistic system as well as the field data. We derive a stochastic model from finite difference approximations of differential equations explaining particle concentrations. Our inferential framework trains the mechanistic system using the field measurements from the chamber experiments and delivers reliable estimates of the underlying physical process with fully model-based uncertainty quantification. Our application falls within the realm of the Bayesian "melding" of mechanistic and statistical models and is of significant relevance to environmental hygienists and public health researchers working on assessing the performance of aerosol removal rates for rail car fleets. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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