Model-based assessment of the risks of viral transmission in non-confined crowds.

This work assesses the risks of Covid-19 spread in diverse daily-life situations involving crowds of maskless pedestrians, mostly outdoors. More concretely, we develop a method to infer the global number of new infections from patchy observations, by coupling ad hoc spatial models for disease transmission via respiratory droplets to detailed field-data about pedestrian trajectories and head orientations. This allows us to rank the investigated situations by the infection risks that they present; importantly, the obtained hierarchy of risks is very largely conserved across transmission models: Street cafés present the largest average rate of new infections caused by an attendant, followed by busy outdoor markets, and then metro and train stations, whereas the risks incurred while walking on fairly busy streets are comparatively quite low.