Pan Soumyakanti, Das Darpan, Ramachandran Gurumurthy, Banerjee Sudipto
Department of Biostatistics, University of California Los Angeles, 650 Charles E. Young Drive South, Los Angeles, CA 90095-1772, United States.
Department of Environment and Geography, Wentworth Way, University of York, Heslington, York Y010 5NG, United Kingdom.
Ann Work Expo Health. 2024 Sep 27;68(8):834-845. doi: 10.1093/annweh/wxae061.
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.
在静止和移动的客运铁路车厢中进行了一系列实验，以测量严重急性呼吸综合征冠状病毒2（SARS-CoV-2）病毒气溶胶尺寸范围内颗粒的去除率，以及现有和改进的空气处理系统每小时的换气次数。此类暴露评估方法通常基于从颗粒运动物理定律推导而来的机械模型，这些模型是确定性的，并未考虑数据收集过程中固有的测量误差。由此产生的分析在从现场测量中可靠地了解通风率、气溶胶产生率和过滤效率等机械因素方面大打折扣。本文提出了一个贝叶斯状态空间建模框架，该框架综合了机械系统和现场数据的信息。我们从解释颗粒浓度的微分方程的有限差分近似中推导出一个随机模型。我们的推理框架利用腔室实验的现场测量数据对机械系统进行训练，并通过基于完全模型的不确定性量化，给出潜在物理过程的可靠估计。我们的应用属于机械模型和统计模型的贝叶斯“融合”领域，对致力于评估铁路车辆车队气溶胶去除率性能的环境卫生学家和公共卫生研究人员具有重要意义。
