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A population framework for predicting the proportion of people infected by the far-field airborne transmission of SARS-CoV-2 indoors

Iddon, Christopher; Jones, Benjamin; Sharpe, Patrick; Cevik, Muge; Fitzgerald, Shaun

A population framework for predicting the proportion of people infected by the far-field airborne transmission of SARS-CoV-2 indoors Thumbnail


Authors

Christopher Iddon

Patrick Sharpe

Muge Cevik

Shaun Fitzgerald



Abstract

The number of occupants in a space influences the risk of far-field airborne transmission of SARS-CoV-2 because the likelihood of having infectious and susceptible people both correlate with the number of occupants. This paper explores the relationship between occupancy and the probability of infection, and how this affects an individual person and a population of people. Mass-balance and dose–response models determine far-field transmission risks for an individual person and a population of people after sub-dividing a large reference space into 10 identical comparator spaces. For a single infected person, the dose received by an individual person in the comparator space is 10 times higher because the equivalent ventilation rate per infected person is lower when the per capita ventilation rate is preserved. However, accounting for population dispersion, such as the community prevalence of the virus, the probability of an infected person being present and uncertainty in their viral load, shows the transmission probability increases with occupancy and the reference space has a higher transmission risk. Also, far-field transmission is likely to be a rare event that requires a high emission rate, and there are a set of Goldilocks conditions that are just right when equivalent ventilation is effective at mitigating against transmission. These conditions depend on the viral load, because when they are very high or low, equivalent ventilation has little effect on transmission risk. Nevertheless, resilient buildings should deliver the equivalent ventilation rate required by standards as minimum.

Citation

Iddon, C., Jones, B., Sharpe, P., Cevik, M., & Fitzgerald, S. (2022). A population framework for predicting the proportion of people infected by the far-field airborne transmission of SARS-CoV-2 indoors. Building and Environment, 221, Article 109309. https://doi.org/10.1016/j.buildenv.2022.109309

Journal Article Type Article
Acceptance Date Jun 14, 2022
Online Publication Date Jun 30, 2022
Publication Date Aug 1, 2022
Deposit Date Sep 9, 2022
Publicly Available Date Mar 29, 2024
Journal Building and Environment
Print ISSN 0360-1323
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 221
Article Number 109309
DOI https://doi.org/10.1016/j.buildenv.2022.109309
Keywords Building and Construction; Geography, Planning and Development; Civil and Structural Engineering; Environmental Engineering
Public URL https://nottingham-repository.worktribe.com/output/8636612
Publisher URL https://www.sciencedirect.com/science/article/pii/S0360132322005431?via%3Dihub

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