The effect of local ventilation on airborne viral transmission in indoor spaces

We incorporate local ventilation effects into a spatially dependent generalisation of the Wells--Riley model of airborne viral transmission. Aerosol production and removal through ventilation (global and local), biological deactivation, and gravitational settling as well as transport around a recirculating air-conditioning flow and turbulent mixing are modelled using an advection--diffusion--reaction equation. The local ventilation effects are compared with the equivalent global ventilation and we find that the streamlines of the airflow provide insight into when the global ventilation model is a good approximation. When the agreement between ventilation models is poor, we find that the global ventilation model generally overestimates the infection risk.Comment: 10 pages, 4 figures, submitted to the Journal of Fluid Mechanics as a Rapids articl

Predicting the spatio-temporal infection risk in indoor spaces using an efficient airborne transmission model

We develop a spatially dependent generalization to the Wells–Riley model, which determines the infection risk due to airborne transmission of viruses. We assume that the infectious aerosol concentration is governed by an advection–diffusion–reaction equation with the aerosols advected by airflow, diffused due to turbulence, emitted by infected people, and removed due to ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person breathing or talking, with or without a mask, and model a quasi-three-dimensional set-up that incorporates a recirculating air-conditioning flow. We derive a semi-analytic solution that enables fast simulations and compare our predictions to three real-life case studies—a courtroom, a restaurant, and a hospital ward—demonstrating good agreement. We then generate predictions for the concentration and the infection risk in a classroom, for four different ventilation settings. We quantify the significant reduction in the concentration and the infection risk as ventilation improves, and derive appropriate power laws. The model can be easily updated for different parameter values and can be used to make predictions on the expected time taken to become infected, for any location, emission rate, and ventilation level. The results have direct applicability in mitigating the spread of the COVID-19 pandemic

THE ROLE OF DONALD TRUMP IN (RE)-SHAPING GLOBAL DEMOCRACY

Bachelor'sBACHELOR OF SOCIAL SCIENCES (HONOURS