Bayesian hierarchical modeling and inference for mechanistic systems in industrial hygiene.

ABSTRACT: 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 a

SUBMITTER: Pan S 

PROVIDER: S-EPMC11427539 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature