Sneezing and asymptomatic virus transmission

Area of Science:

• Fluid dynamics
• Biomedical engineering
• Epidemiology

Background:

• Geographical barriers are insufficient to contain viral spread, as evidenced by COVID-19.
• Asymptomatic carriers play a significant role in the rapid escalation of pandemics.
• Unintentional transmission via sneezing highlights the need for accurate spread modeling.

Purpose of the Study:

• To propose a novel Computational Fluid Dynamics (CFD) approach for realistic human sneeze modeling.
• To enable accurate prediction of aerosol and droplet spread containing pathogens.
• To inform engineering analyses for reshaping public spaces to minimize disease transmission.

Main Methods:

• Combined state-of-the-art experimental and numerical methods for sneeze simulation.
• Modeled sneeze biomechanics using angular head motion and dynamic pressure response (human factors).
• Implemented a momentum source term in Eulerian-Lagrangian equations, eliminating the need for ad hoc inlet conditions.

Main Results:

• Accurate modeling of complex muscle contractions and relaxations during sneezing.
• Demonstrated the feasibility of incorporating multiple sneeze sources in complex domains.
• Quantified the impact of environmental conditions on potential virus spread through sensitivity analyses.

Conclusions:

• The proposed CFD approach accurately models human sneeze biomechanics.
• This method facilitates the prediction of pathogen spread in public spaces.
• The findings support engineering strategies for enhanced public health safety.