A computational model for predicting changes in infection dynamics due to leakage through N95 respirators

Area of Science:

• * Aerosol science and respiratory protection.
• * Computational fluid dynamics (CFD) and risk assessment modeling.

Background:

• * Inadequate fit of N95 respirators allows pathogen leakage, increasing infection risk.
• * Current fit-testing methods are essential but can be resource-intensive.

Purpose of the Study:

• * To develop and validate a model estimating infection risk from N95 respirator leakage.
• * To quantify aerosol transmission through face-respirator gaps using CFD.
• * To assess the impact of respirator fit on infection rates during an influenza outbreak.

Main Methods:

• * Computed tomography (CT) scans to capture face-respirator gap geometry.
• * Computational fluid dynamics (CFD) simulations to predict aerosol leakage.
• * Experimental validation using manikins and comparison with risk assessment models.

Main Results:

• * CFD leakage predictions showed ~13% variance from experimental data.
• * Inward aerosol leakage ranged from 30% to 95% based on respirator fit.
• * Non-fit-tested respirators reduced infection rates from 97% to 42-80%, compared to 12% with fit-tested ones.

Conclusions:

• * A validated CFD-based leakage model can accurately predict aerosol transmission.
• * Respirator fit significantly impacts infection risk, underscoring the importance of fit-testing.
• * The integrated modeling approach aids in optimizing population-level protection strategies against airborne pathogens.