One size fits all?: Modeling face-mask fit on population-based faces

Abstract

AbstractThe use of face masks by the general population during viral outbreaks such as the COVID-19 pandemic, although at times controversial, has been effective in slowing down the spread of the virus. The fit of simple cloth masks on the face as well as the resulting perimeter leakage and face mask efficacy are expected to be highly dependent on the type of mask and facial topology. However, this effect has to date, not been examined and quantified. Here, we study the leakage of a rectangular cloth mask on a large virtual population of subjects with diverse facial features, using computational mechanics modeling. The effect of weight, age, gender, and height on the leakage is studied. The Centers for Disease Control and Prevention (CDC) recommended mask size was used as a basis for comparison and was found not to be the most effective design for all subjects. Thin, feminine, and young faces benefit from mask sizes smaller than that recommended by the CDC. The results show that side-edge tuck-in of the masks could lead to a larger localized gap opening in many face categories, and is therefore not recommended for all. The perimeter leakage from the face mask worn by thin/feminine faces is mostly from the leakage area along the bottom edge of the mask and therefore, a tuck-in of the bottom edge of the mask or a mask smaller than the CDC recommended mask size are proposed as a more effective design. The leakage from the top edge of the mask is determined to be largely unaffected by mask size and tuck-in ratio, meaning that other mechanical alterations such as a nose wire strip are necessary to reduce the leakage at this site.