  A numerical framework for the 'real-time' estimation of the infection risk
from airborne diseases (e.g., SARS-CoV-2) in indoor spaces such as hospitals,
restaurants, cinemas or teaching rooms is proposed. The developed model is
based on the use of computational fluid dynamics as a pre-processor to obtain
the time-averaged ventilation pattern inside a room, and a post-processing tool
for the computation of the dispersion of sprays and aerosols emitted by its
occupants in 'real time'. The model can predict the dispersion and
concentration of droplets carrying viable viral copies in the air, the
contamination of surfaces, and the related spatially-resolved infection risk.
It may therefore provide useful information for the management of indoor
environments in terms of, e.g., maximum occupancy, air changes per hour and
cleaning of surfaces. This work describes the fundamentals of the model and its
main characteristics. The model was developed using open-source software and is
conceived to be simple, user-friendly and highly automated to enable any
potential user to perform estimations of the local infection risk.
