Predicting the Spatially Varying Infection Risk in Indoor Spaces Using an Efficient Airborne Transmission Model

Autor: Lau, Zechariah, Griffiths, Ian M., English, Aaron, Kaouri, Katerina
Rok vydání: 2020
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Druh dokumentu: Working Paper
Popis: We develop a spatially dependent generalisation to the Wells-Riley model and its extensions applied to COVID-19, 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 derive power laws that quantify the effect of ventilation, emission rate and infectiousness of the virus on the TTPI. The model can be easily updated to take into account modified parameter values. This work paves the way for establishing `safe occupancy times' at any location and has direct applicability in mitigating the spread of the COVID-19 pandemic.
Comment: 25 pages, 14 figures. Version v3 substantially revises v1. (v3 same as v2 but with a more compact layout, hence fewer pages.) Compared to v1 the model has been extended to determine the spatiotemporal infection risk and recast as an extension of the Wells-Riley model. Another author has been added. The underlying model and domain linked to the semi-analytic solution are now explained in detail
Databáze: arXiv
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