2023/12/04 by Alexandre Nicolas, Nicolas, Alexandre, Simon Mendez +1
Engineering · Mathematics · #COVID-19 epidemiological studies #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #FOS: Physical sciences #Multiagent Systems (cs.MA) #Physics and Society (physics.soc-ph)
paper · doi:10.48550/arxiv.2312.01779
openalex publication_date 2023/12/04 · openalex created_date 2023/12/06 · openalex updated_date 2026/07/28
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.