Modeling Airborne Virus Transmission Risk in a Controlled Exposure Clinical Study

Abstract

Airborne transmission of viral aerosols exhaled by infected individuals is a major pathway for the spread of respiratory diseases. To effectively control aerosol transmission, designing and implementing effective air cleaning solutions is essential in public spaces. Achieving this goal needs a deep understanding of viral aerosol transport patterns and accurate estimation of occupant exposure to viral aerosols. Accordingly, the current dissertation presents a comprehensive research framework for the design of effective air cleaning systems and occupant exposure estimation in public spaces through (i) defining reliable k-values for critical microorganisms, which are essential for accurately modeling and designing germicidal ultraviolet (GUV) air cleaning systems, (ii) Floor-level analysis of viral aerosol transport and air cleaning system design, and (iii) estimation of individual exposure of occupants at the zone-level. To address the high variability of k-values reported in the literature for microorganisms, the dissertation identifies representative k-values of critical pathogens and their surrogates across different media by analyzing the results of a comprehensive literature review of 360 studies. The study examines the influence of UV wavelength, light source, and environmental conditions on the k-values. The study specifies potential surrogates for different pathogens. Results further reveal that k-values measured in liquid or on solid surfaces are generally lower than those in the aerosolized state, suggesting that surface- or liquid-phase data may serve as conservative estimates when aerosol data are unavailable. At the floor-level, the present study evaluates viral aerosol transport and the performance of different air cleaning strategies by developing a validated multi-zone model. The study identifies shared spaces and the corridor ends as critical zones for aerosol transmission, necessitating targeted air cleaning interventions. The study developed an index, named the inter-zonal air exchange rate (zACH), to quantify the ability of air cleaning systems to reduce inter-zonal aerosol transmission. The results show that the scenario using only GUV systems outperforms those using only HEPA air cleaners or filtered air curtains. More importantly, the study shows that layered air cleaning strategies integrating these systems perform better than single-system scenarios by leveraging their complementary strengths. To scrutinize aerosol transport within shared spaces, as very critical areas, and estimate individual occupant exposures, the dissertation develops a methodology combining steady-state CFD for characterizing the exhaled viral aerosol plume with computer vision analysis for dynamic occupant tracking. This approach accounts for variability in both environmental and behavioral factors. The study provides recommendations for selecting appropriate exposure assessment methods based on indoor airflow and respiratory conditions. Specifically, for spaces where breathing and speaking are dominant respiratory activities, it suggests using transient CFD and computer vision, steady-state CFD and computer vision, and fully-mixed models for low, moderate, and high air mixing conditions, respectively. The proposed methodology establishes a framework for developing integrated airflow–respiratory interaction datasets that can be combined with computer vision tools for exposure analysis in public spaces.