Modelling airborne transmission of SARS-CoV-2: Risk assessment for enclosed spaces

The global crisis triggered by the Coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for a proper risk assessment of respiratory pathogens in indoor settings, due to their potential for airborne transmission. This paper is intended to document the COVID Airborne Risk Assessment (CARA) methodology, tailored for typical work spaces or public environments, enabling a quick and easy assessment of the potential exposure to SARS-CoV-2. A physical model is presented to compute the absorbed dose of virions from an exposed host, which leads to estimate the probability of contracting the disease based on the stochastic interpretation of the Wells-Riley model. The model allows for a detailed parameterization of the indoor setting, with emphasis on the effect of natural ventilation and air filtration, enabling decision makers or facility managers to perform risk assessments against airborne transmission of SARS-CoV-2. The results suggest the importance of super-emitters and superspreading events in airborne transmission: i) a small subset of infected hosts are found to emit approximately 2 orders of magnitude more viral-containing particles for any given expiratory activity and ii) loud vocalisation activities (singing or shouting) generate approximately 2 orders of magnitude more airborne particles, compared to tidal breathing. The effect of air filtration and natural ventilation suggests that i) HEPA filters significantly reduce inhaled dose of airborne viruses by a factor 5.3 in classrooms with windows closed and ii) natural ventilation strategies shall be adapted to the seasonal period since it is twice as effective during winter, compared to summer. Furthermore, the approach of a viral load threshold value was introduced, where the effect of different measures can be physically tuned such that the transmission is unlikely to occur for a given indoor setting. The properties of emerging new SARS-CoV-2 Variant of Concern (VOC) is included in the model.