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Atamer Balkan B, Chang Y, Sparnaaij M, Wouda B, Boschma D, Liu Y, Yuan Y, Daamen W, de Jong MCM, Teberg C, Schachtschneider K, Sikkema RS, van Veen L, Duives D, ten Bosch QA. The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model. PLoS Comput Biol 2024; 20:e1011956. [PMID: 38547311 PMCID: PMC11003685 DOI: 10.1371/journal.pcbi.1011956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 04/09/2024] [Accepted: 02/29/2024] [Indexed: 04/11/2024] Open
Abstract
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 incorporating 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 the buildup 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. This uncertainty should be considered when assessing transmission risks upon different types of human interactions in indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions.
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Affiliation(s)
- Büsra Atamer Balkan
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - You Chang
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Martijn Sparnaaij
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Berend Wouda
- Gamelab, Delft University of Technology, Delft, The Netherlands
| | - Doris Boschma
- Gamelab, Delft University of Technology, Delft, The Netherlands
| | - Yangfan Liu
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Yufei Yuan
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Winnie Daamen
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Mart C. M. de Jong
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Colin Teberg
- Steady State Scientific Computing, Chicago, Illinois, United States of America
| | | | | | - Linda van Veen
- Gamelab, Delft University of Technology, Delft, The Netherlands
| | - Dorine Duives
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Quirine A. ten Bosch
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
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