دورية أكاديمية
Bayesian hierarchical modeling and inference for mechanistic systems in industrial hygiene.
| العنوان: | Bayesian hierarchical modeling and inference for mechanistic systems in industrial hygiene. |
|---|---|
| المؤلفون: | Pan S; Department of Biostatistics, University of California Los Angeles, 650 Charles E. Young Drive South, Los Angeles, CA 90095-1772, United States., Das D; Department of Environment and Geography, Wentworth Way, University of York, Heslington, York Y010 5NG, United Kingdom., Ramachandran G; Department of Environmental Health Sciences and Engineering, Johns Hopkins Bloomberg School of Public Health and Whitmore School of Engineering, 615 N. Wolfe Street, Baltimore, MD 21205, United States., Banerjee S; Department of Biostatistics, University of California Los Angeles, 650 Charles E. Young Drive South, Los Angeles, CA 90095-1772, United States. |
| المصدر: | Annals of work exposures and health [Ann Work Expo Health] 2024 Sep 27; Vol. 68 (8), pp. 834-845. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: England NLM ID: 101698454 Publication Model: Print Cited Medium: Internet ISSN: 2398-7316 (Electronic) Linking ISSN: 23987308 NLM ISO Abbreviation: Ann Work Expo Health Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: [Oxford] : Oxford University Press, [2017]- |
| مواضيع طبية MeSH: | Bayes Theorem* , COVID-19*/transmission , Aerosols*/analysis , SARS-CoV-2*, Humans ; Occupational Exposure/analysis ; Occupational Exposure/statistics & numerical data ; Occupational Health ; Ventilation/methods ; Environmental Monitoring/methods ; Railroads ; Models, Statistical |
| مستخلص: | 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. (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Occupational Hygiene Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.) |
| معلومات مُعتمدة: | R01ES030210 United States ES NIEHS NIH HHS; R01GM148761 National Institute of General Medical Science; 2113778 Division of Mathematical Sciences (DMS) of the National Science Foundation; R01ES030210 United States ES NIEHS NIH HHS |
| فهرسة مساهمة: | Keywords: Bayesian inference; differential equations; dynamical systems; industrial hygiene; mechanistic systems; melding; state-space models |
| المشرفين على المادة: | 0 (Aerosols) |
| تواريخ الأحداث: | Date Created: 20240724 Date Completed: 20240927 Latest Revision: 20240927 |
| رمز التحديث: | 20240927 |
| DOI: | 10.1093/annweh/wxae061 |
| PMID: | 39046904 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 2398-7316 |
|---|---|
| DOI: | 10.1093/annweh/wxae061 |