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Estimating COVID-19 exposure in a classroom setting: A comparison between mathematical and numerical models
Aaron Foster1, Michael Kinzel1
1University of Central Florida, Mechanical and Aerospace Engineering, Orlando, Florida 32766, USA.
Physics of Fluids (Woodbury, N.Y. : 1994)
|March 22, 2021
Summary
Comparing airborne transmission models, Wells-Riley and computational fluid dynamics (CFD), revealed significant errors with forced ventilation. Moderate filtration and mask use effectively reduce COVID-19 transmission in classrooms.
Area of Science:
- Environmental Health Engineering
- Infectious Disease Transmission Modeling
Background:
- The COVID-19 pandemic spurred research into airborne transmission routes.
- Wells-Riley and computational fluid dynamics (CFD) are primary modeling methods.
- Comparing these models is crucial for understanding classroom transmission risks.
Purpose of the Study:
- To compare Wells-Riley and CFD models for airborne transmission in a classroom.
- To evaluate the impact of ventilation and mask-wearing on transmission.
- To analyze factors influencing infection probability variations.
Main Methods:
- Simulated a classroom scenario with masked occupants.
- Compared Wells-Riley model predictions with CFD simulation results.
- Investigated various natural and forced ventilation conditions.
Main Results:
- Wells-Riley models showed good agreement with CFD without forced ventilation (6% error).
- Significant discrepancies arose with forced ventilation (29% error).
- Moderate filtration substantially reduced infection probability.
- Local air patterns and proximity were key drivers of transmission variation.
- Masks mitigated aerosol spread via thermal plume interaction.
Conclusions:
- CFD models are more accurate for assessing forced ventilation impacts on airborne transmission.
- Ventilation with filtration and mask usage are effective mitigation strategies.
- Understanding airflow dynamics is critical for accurate transmission risk assessment.
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