Predicting the Spatially Varying Infection Risk in Indoor Spaces Using an Efficient Airborne Transmission Model (original) (raw)

We develop a spatially dependent generalisation to the Wells–Riley model [1] and its extensions applied to COVID-19, for example [2], that determines the infection risk due to airborne transmission of viruses. We assume that the concentration of infectious particles is governed by an advection–diffusion–reaction equation with the particles advected by airflow, diffused due to turbulence, emitted by infected people and removed due to the room ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person who breathes or talks, with or without a mask and model a quasi-3D setup that incorporates a recirculating air-conditioning flow. A semi-analytic solution is available and this enables fast simulations. We quantify the effect of ventilation and particle emission rate on the particle concentration, infection risk and the ‘time to probable infection’ (TTPI). Good agreement with CFD models is achieved. Furthermore, we ...

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