From Microscopic Droplets to Macroscopic Crowds: Crossing the Scales in Models of Short-Range Respiratory Disease Transmission, with Application to COVID-19.
crowd dynamics
epidemiology
fluid dynamics
respiratory droplets
Journal
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
ISSN: 2198-3844
Titre abrégé: Adv Sci (Weinh)
Pays: Germany
ID NLM: 101664569
Informations de publication
Date de publication:
07 2023
07 2023
Historique:
revised:
14
03
2023
received:
13
09
2022
medline:
7
7
2023
pubmed:
3
5
2023
entrez:
3
5
2023
Statut:
ppublish
Résumé
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). At the individual scale, the results highlight the paramount importance of the velocity of the ambient air flow relative to the emitter's motion. This aerodynamic effect, which disperses infectious aerosols, prevails over all other environmental variables. At the crowd's scale, the method yields a ranking of the scenarios by the risks of new infections, dominated by the street cafés and then the outdoor market. While the effect of light winds on the qualitative ranking is fairly marginal, even the most modest air flows dramatically lower the quantitative rates of new infections.
Identifiants
pubmed: 37132608
doi: 10.1002/advs.202205255
pmc: PMC10323631
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2205255Subventions
Organisme : Agence Nationale de la Recherche
ID : ANR-20-COV1-0003
Organisme : Agence Nationale de la Recherche
ID : ANR-21-CO15-0002
Informations de copyright
© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.
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