Talking and Facemasks: Unveiling the Crucial Factors for Optimal Protection of Large Population

Facemasks have proven their effectiveness in reducing the transmission of airborne viruses. However, their true efficacy depends on several factors, such as how well they fit, the shape of the wearer's face, respiratory patterns, filtration efficiency, and interactions with the mask materials. One significant concern is that during routine respiratory activities like talking, the shape of the face changes continuously, leading to gaps forming around the edges of the facemask. These gaps can have a substantial impact on the mask's overall effectiveness in protecting against virus transmission. To address this issue, our study utilized advanced machine learning and fast physics-based modeling techniques to create minimal surrogate models for a large virtual population. We mathematically represented the space between the face and the mask as interconnected channels with a porous top boundary. This allowed us to establish compatibility conditions and determine how the airflow leaks around the edge of the mask during various scenarios, including talking, and considering a diverse range of face shapes. We discuss simple metrics to quantify leakage patterns and assess mask efficacy during talking activities. Ultimately, this research can contribute to improving facemask designs and their overall performance in mitigating virus transmission.