The COVID-19 pandemic has driven numerous studies of airborne-driven transmission risk primarily through two methods: Wells-Riley and computational fluid dynamics (CFD) models. This effort provides a detailed comparison of the two methods for a classroom scenario with masked habitants and various ventilation conditions. The results of the studies concluded that (1) the Wells-Riley model agrees with CFD results without forced ventilation (6% error); (2) for the forced ventilation cases, there was a significantly higher error (29% error); (3) ventilation with moderate filtration is shown to significantly reduce infection transmission probability in the context of a classroom scenario; (4) for both cases, there was a significant amount of variation in individual transmission route infection probabilities (up to 220%), local air patterns were the main contributor driving the variation, and the separation distance from infected to susceptible was the secondary contributor; (5) masks are shown to have benefits from interacting with the thermal plume created from natural convection induced from body heat, which pushes aerosols vertically away from adjacent students.
All Science Journal Classification (ASJC) codes
- Computational Mechanics
- Condensed Matter Physics
- Mechanics of Materials
- Mechanical Engineering
- Fluid Flow and Transfer Processes