Large eddy simulation of droplet transport and deposition in the human respiratory tract to evaluate inhalation risk.

As evidenced by the worldwide pandemic, respiratory infectious diseases and their airborne transmission must be studied to safeguard public health. This study focuses on the emission and transport of speech-generated droplets, which can pose risk of infection depending on the loudness of the speech, its duration and the initial angle of exhalation. We have numerically investigated the transport of these droplets into the human respiratory tract by way of a natural breathing cycle in order to predict the infection probability of three strains of SARS-CoV-2 on a person who is listening at a one-meter distance. Numerical methods were used to set the boundary conditions of the speaking and breathing models and large eddy simulation (LES) was used for the unsteady simulation of approximately 10 breathing cycles. Four different mouth angles when speaking were contrasted to evaluate real conditions of human communication and the possibility of infection. Breathed virions were counted using two different approaches: the breathing zone of influence and direction deposition on the tissue. Our results show that infection probability drastically changes based on the mouth angle and the breathing zone of influence overpredicts the inhalation risk in all cases. We conclude that to portray real conditions, the probability of infection should be based on direct tissue deposition results to avoid overprediction and that several mouth angles must be considered in future analyses. Author summary: We have experienced the devastating nature of airborne transmitted diseases through the recent COVID-19 pandemic. To eradicate short and long-range cross-infection of viruses like SARS-CoV-2 and other such diseases, we need to unravel the fundamentals of droplet generation, transport and deposition due to human interactions. Here, we have computationally simulated viral droplet transport from one talking person to another and calculated the infection probability by predicting the exact number of virions attached to the respiratory tract tissue of the possible host. Our results emphasize the need of using real-like settings and conditions for virtual infection risk prediction to avoid under or overestimation. The ability to accurately predict infection probability of SARS-CoV-2 and other airborne transmitted diseases through computer simulations benefits society by enabling us to create new guidelines and preventive measures for present and future needs. [ABSTRACT FROM AUTHOR]