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