TY - JOUR
T1 - The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces
T2 - Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model
AU - Balkan, Büsra Atamer
AU - Chang, You
AU - Sparnaaij, Martijn
AU - Wouda, Berend
AU - Boschma, Doris
AU - Liu, Yangfan
AU - Yuan, Yufei
AU - Daamen, Winnie
AU - de Jong, C. M.
AU - Teberg, Colin
AU - Schachtschneider, Kevin
AU - Sikkema, Reina S.
AU - van Veen, Linda
AU - Duives, Dorine
AU - ten Bosch, Quirine A.
N1 - Publisher Copyright:
© 2024 Atamer Balkan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,
PY - 2024/3/28
Y1 - 2024/3/28
N2 - SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics—Virus Spread model (PeDViS): an individual-based model that combines pedestrian behaviour models with virus spread models that incorporate direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark ‘at risk’ interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to build-up of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent.
AB - SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics—Virus Spread model (PeDViS): an individual-based model that combines pedestrian behaviour models with virus spread models that incorporate direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark ‘at risk’ interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to build-up of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent.
UR - http://www.scopus.com/inward/record.url?scp=85188926682&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1011956
DO - 10.1371/journal.pcbi.1011956
M3 - Article
C2 - 38547311
AN - SCOPUS:85188926682
SN - 1553-734X
VL - 20
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 3
M1 - 1011956
ER -