  Epidemiology models play a key role in understanding and responding to the
COVID-19 pandemic. In order to build those models, scientists need to
understand contributing factors and their relative importance. A large strand
of literature has identified the importance of airflow to mitigate droplets and
far-field aerosol transmission risks. However, the specific factors
contributing to higher or lower contamination in various settings have not been
clearly defined and quantified. As part of the MOAI project
(https://moaiapp.com), we are developing a privacy-preserving test and trace
app to enable infection cluster investigators to get in touch with patients
without having to know their identity. This approach allows involving users in
the fight against the pandemic by contributing additional information in the
form of anonymous research questionnaires. We first describe how the
questionnaire was designed, and the synthetic data was generated based on a
review we carried out on the latest available literature. We then present a
model to evaluate the risk exposition of a user for a given setting. We finally
propose a temporal addition to the model to evaluate the risk exposure over
time for a given user.
