From Microscopic Droplets to Macroscopic Crowds: Crossing the Scales in Models of Short-Range Respiratory Disease Transmission, with Application to COVID-19.

Short-range exposure to airborne virus-laden respiratory droplets is an effective transmission route of respiratory diseases, as exemplified by Coronavirus Disease 2019 (COVID-19). In order to assess the risks associated with this pathway in daily-life settings involving tens to hundreds of individuals, the chasm needs to be bridged between fluid dynamical simulations and population-scale epidemiological models. This is achieved by simulating droplet trajectories at the microscale in numerous ambient flows, coarse-graining their results into spatio-temporal maps of viral concentration around the emitter, and coupling these maps to field-data about pedestrian crowds in different scenarios (streets, train stations, markets, queues, and street cafés).

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.