This RAPID project will advance the understanding of how infectious diseases are spread from speaking, which is directly relevant to informing decisions regarding social distancing measures in the current COVID-19 pandemic. During regular conversation, small virus-laden droplets are generated in the respiratory and vocal tract. They are then expelled into the surrounding environment as airflow exits the mouth. Larger droplets may impact surfaces in the immediate vicinity, while smaller ones may stay suspended in the air as aerosols for long periods of time. Both types of expiratory droplets pose an infection risk. Larger droplets may land directly on individuals that are in close proximity, while smaller aerosolized particles are capable of traveling longer distances and infecting people much farther away by being inhaled into the respiratory tract. Currently, the range of distances that particles travel when produced by speech, and the resulting infection risk posed to individuals in the immediate (near field) and more distant (far field) vicinity, is not well understood. The potential for increased infection risk due to prolonged speech in indoor environments, where airborne particles may accumulate over time and reach higher concentrations, is also not known. This research proposal aims to answer these questions by performing physical measurements of speech in tandem with numerical modeling of droplet transport to predict infection risks in both the near and far field of an infected speaker.
The goal of this project is to determine how virus-laden droplets are spread during speech. Particular emphasis is placed on investigating specific vocal intonations that produce high velocity bursts of air at the mouth (i.e., fricatives and plosives), which are hypothesized to propel particles over relatively long distances and increase infection risk in both the near and far field. Experimental velocity field measurements of the human mouth jet will be acquired using particle image velocimetry for sustained vowels, fricatives, and plosives, at varying levels of loudness. Separately, aerodynamic particle size spectrometry will measure the droplet size distributions generated by these same utterances. These data will be used to validate and inform an unsteady Reynolds-averaged Navier-Stokes computational fluid dynamics model that adopts an Eulerian-Lagrangian approach to model and quantify droplet dynamics. Coupling the expiratory velocity fields and particle dynamics for specific speech utterances will facilitate predicting the infection risk arising from voiced speech by using viral titer levels and infectious doses for the SARS-CoV-2 virus. Near and far field infection risks will be quantified for both open and confined surroundings, where the competing times scales associated with ventilation, particle settling, and virus inactivation rates, will influence the steady-state particle accumulation. Mitigation strategies to decrease infection risk in indoor environments will also be explored.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
