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We study viral transmission in crowds via the short-ranged airborne pathwayusing a purely model-based approach. Our goal is two-pronged. Firstly, weillustrate with a concrete and pedagogical case study how to estimate the risksof new viral infections by coupling pedestrian simulations with thetransmission algorithm that we recently released as open-source code. Thealgorithm hinges on pre-computed viral concentration maps derived fromcomputational fluid dynamics (CFD) simulations. Secondly, we investigate towhat extent the transmission risk predictions depend on the pedestrian dynamicsmodel in use. For the simple bidirectional flow under consideration, thepredictions are found to be surprisingly stable across initial conditions andmodels, despite the different microscopic arrangements of the simulated crowd,as long as the crowd evolves in a qualitatively similarly way. On the otherhand, when major changes are observed in the crowd's behaviour, notablywhenever a jam occurs at the centre of the channel, the estimated risks surgedrastically.

Viral transmission in pedestrian crowds: Coupling an open-source code  assessing the risks of airborne contagion with diverse pedestrian dynamics  models