MOAI: A methodology for evaluating the impact of indoor airflow in the transmission of COVID-19 Axel Oehmichen*1,2, Florian Guitton*1,2, Cedric Wahl2, Bertrand Foing2, Damian Tziamtzis2, and Yike Guo1 1 Data Science Institute, Imperial College London, United-Kingdom 2 Secretarium Ltd, London, United-Kingdom f axelfrancois.oehmichen11, f.guitton, yg
[email protected] Abstract—Epidemiology models play a key role in understand- (e.g. UK, Ireland, Germany or Denmark) of opening and clos- ing and responding to the COVID-19 pandemic. In order to ing over time in various countries based on evolving scientific build those models, scientists need to understand contributing consensus. Some NPIs have proven counterproductive as well, factors and their relative importance. A large strand of literature has identified the importance of airflow to mitigate droplets and even though it might appear counter-intuitive, such as one far-field aerosol transmission risks. However, the specific factors way systems [1]. Nevertheless, in the absence of a precise contributing to higher or lower contamination in various settings understanding of the transmission mechanisms of COVID-19, have not been clearly defined and quantified. the NPIs put in place are fairly broad and coarse, which puts As part of the MOAI project (https://moaiapp.com), we are more pressure on our economies, mental health and personal developing a privacy-preserving test and trace app to enable infection cluster investigators to get in touch with patients without liberties. In order to alleviate that pressure, a more precise having to know their identity. This approach allows involving understanding of closed spaces exposure risks is necessary.