Modelling airborne transmission of SARS-CoV-2 using CARA: risk assessment for enclosed spaces.
Andre HenriquesNicolas MounetLuis AleixoPhilip ElsonJames DevineGabriella AzzopardiMarco AndreiniMarkus RognlienNicola TaroccoJulian Wei-Tze TangPublished in: Interface focus (2022)
The COVID-19 pandemic has highlighted the need for a proper risk assessment of respiratory pathogens in indoor settings. This paper documents the COVID Airborne Risk Assessment methodology, to assess the potential exposure of airborne SARS-CoV-2 viruses, with an emphasis on virological and immunological factors in the quantification of the risk. The model results from a multidisciplinary approach linking physical, mechanical and biological domains, enabling decision makers or facility managers to assess their indoor setting. The model was benchmarked against clinical data, as well as two real-life outbreaks, showing good agreement. A probability of infection is computed in several everyday-life settings and with various mitigation measures. The importance of super-emitters in airborne transmission is confirmed: 20% of infected hosts can emit approximately two orders of magnitude more viral-containing particles. The use of masks provides a fivefold reduction in viral emissions. Natural ventilation strategies are very effective to decrease the concentration of virions, although periodic venting strategies are not ideal in certain settings. Although vaccination is an effective measure against hospitalization, their effectiveness against transmission is not optimal, hence non-pharmaceutical interventions (ventilation, masks) should be actively supported. We also propose a critical threshold to define an acceptable risk level.
Keyphrases
- sars cov
- particulate matter
- risk assessment
- air pollution
- human health
- respiratory syndrome coronavirus
- heavy metals
- physical activity
- randomized controlled trial
- systematic review
- respiratory failure
- coronavirus disease
- magnetic resonance imaging
- mechanical ventilation
- big data
- hiv infected
- intensive care unit
- antiretroviral therapy
- machine learning
- antimicrobial resistance
- diffusion weighted imaging
- breast cancer risk