Evidence map›Paper›PMID 38547311›Full record

ArticlePLoS computational biology2024

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.

Büsra Atamer Balkan, You Chang, Martijn Sparnaaij, Berend Wouda, Doris Boschma, Yangfan Liu, Yufei Yuan, Winnie Daamen, Mart C M de Jong, Colin Teberg and 5 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Büsra Atamer BalkanQuantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands.ORCID 0000-0002-5950-1606
You ChangQuantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands.
Martijn SparnaaijDepartment of Transport & Planning, Delft University of Technology, Delft, The Netherlands.ORCID 0000-0001-5115-303X
Berend WoudaGamelab, Delft University of Technology, Delft, The Netherlands.
Doris BoschmaGamelab, Delft University of Technology, Delft, The Netherlands.
Yangfan LiuQuantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands.ORCID 0000-0003-0812-8853
Yufei YuanDepartment of Transport & Planning, Delft University of Technology, Delft, The Netherlands.
Winnie DaamenDepartment of Transport & Planning, Delft University of Technology, Delft, The Netherlands.
Mart C M de JongQuantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands.
Colin TebergSteady State Scientific Computing, Chicago, Illinois, United States of America.
Kevin SchachtschneiderSteady State Scientific Computing, Chicago, Illinois, United States of America.
Reina S SikkemaViroScience, Erasmus Medical Center, Rotterdam, The Netherlands.
Linda van VeenGamelab, Delft University of Technology, Delft, The Netherlands.
Dorine DuivesDepartment of Transport & Planning, Delft University of Technology, Delft, The Netherlands.
Quirine A Ten BoschQuantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands.ORCID 0000-0002-5227-1406

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19PedestriansHumansRespiratory Aerosols and DropletsSARS-CoV-2Ventilation

Identifiers

PMID38547311
PMCPMC11003685

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.