One size fits all?: A simulation framework for face-mask fit on population-based faces.

Area of Science:

• Mechanical engineering
• Biomedical engineering
• Public health

Background:

• Face mask efficacy in mitigating viral transmission is crucial during pandemics like COVID-19.
• Mask fit and perimeter leakage significantly influence protection, yet this is not well-quantified.
• Facial topology and mask design are key factors affecting mask fit and effectiveness.

Purpose of the Study:

• To develop and utilize a computational framework for assessing face mask efficacy based on fit.
• To quantify the impact of mask design and facial variations on mask fit and leakage.
• To evaluate the effectiveness of different mask designs, including CDC recommendations, across diverse facial types.

Main Methods:

• A quasi-static mechanical model was employed to simulate mask deployment on virtual faces.
• A large virtual population was generated using a 3D morphable face model.
• Aerodynamic principles were used to analyze mask fit, leakage, and efficacy, considering factors like weight, age, gender, and height.

Main Results:

• The study revealed that mask fit is highly dependent on individual facial characteristics.
• The Centers for Disease Control and Prevention (CDC) recommended mask design was not universally optimal.
• Smaller mask sizes benefited thin, feminine, and young faces, while tucking or pleating could create gaps.

Conclusions:

• Face mask design must be individualized to account for diverse facial morphologies.
• Optimizing mask fit through adjusted sizing and design features can enhance protection against viral spread.
• Further research into personalized mask design is essential for maximizing public health interventions during epidemics.