A population-based study of the facemask fluid dynamics during talking activity

Recent studies have shown that the flow generated during routine daily activities such as talking, breathing, and laughing has the potential to spread the SARS-CoV-2 virus. Droplets produced during everyday speech may act as a catalyst in the transmission of COVID-19. Here, we focus on understanding how some syllables may induce mouth movement and further may result in a poor fit of the mask during talking, resulting in modified leakages from the gaps. A novel analytical integral boundary layer solution is devised to quantify the flow in the interface region between the face and the mask. The mask is deployed on the face using the concept of minimum elastic energy to ensure a natural fit. The space between the face and the mask is mathematically modeled with a series of interconnected channels with the porous top boundary, wherein the compatibility conditions determine the channel flow distribution. The model is validated with a detailed flow simulation and employed to find the interconnected relation between fitness during talking, porosity, and leakage through the mask. We quantify the effectiveness of face masks during talking in a large cohort of faces and diverse probable talking scenarios to advise simple metrics that can quantify the leakage pattern and mask efficacy in talking activities.