Imperial College London
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DrAdaYan

Faculty of Medicine, Department of Infectious Disease

Imperial College Research Fellow
 
 
 
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Contact

 

a.yan Website

 
 
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Location

 

421Praed StreetSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Deol:2021:10.1371/journal.pone.0253096,
author = {Deol, AK and Scarponi, D and Beckwith, P and Yates, TA and Karat, AS and Yan, AWC and Baisley, KS and Grant, AD and White, RG and McCreesh, N},
doi = {10.1371/journal.pone.0253096},
journal = {PLoS One},
title = {Estimating ventilation rates in rooms with varying occupancy levels: Relevance for reducing transmission risk of airborne pathogens},
url = {http://dx.doi.org/10.1371/journal.pone.0253096},
volume = {16},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: In light of the role that airborne transmission plays in the spread of SARS-CoV-2, as well as the ongoing high global mortality from well-known airborne diseases such as tuberculosis and measles, there is an urgent need for practical ways of identifying congregate spaces where low ventilation levels contribute to high transmission risk. Poorly ventilated clinic spaces in particular may be high risk, due to the presence of both infectious and susceptible people. While relatively simple approaches to estimating ventilation rates exist, the approaches most frequently used in epidemiology cannot be used where occupancy varies, and so cannot be reliably applied in many of the types of spaces where they are most needed. METHODS: The aim of this study was to demonstrate the use of a non-steady state method to estimate the absolute ventilation rate, which can be applied in rooms where occupancy levels vary. We used data from a room in a primary healthcare clinic in a high TB and HIV prevalence setting, comprising indoor and outdoor carbon dioxide measurements and head counts (by age), taken over time. Two approaches were compared: approach 1 using a simple linear regression model and approach 2 using an ordinary differential equation model. RESULTS: The absolute ventilation rate, Q, using approach 1 was 2407 l/s [95% CI: 1632-3181] and Q from approach 2 was 2743 l/s [95% CI: 2139-4429]. CONCLUSIONS: We demonstrate two methods that can be used to estimate ventilation rate in busy congregate settings, such as clinic waiting rooms. Both approaches produced comparable results, however the simple linear regression method has the advantage of not requiring room volume measurements. These methods can be used to identify poorly-ventilated spaces, allowing measures to be taken to reduce the airborne transmission of pathogens such as Mycobacterium tuberculosis, measles, and SARS-CoV-2.
AU  - Deol,AK
AU  - Scarponi,D
AU  - Beckwith,P
AU  - Yates,TA
AU  - Karat,AS
AU  - Yan,AWC
AU  - Baisley,KS
AU  - Grant,AD
AU  - White,RG
AU  - McCreesh,N
DO  - 10.1371/journal.pone.0253096
PY  - 2021///
SN  - 1932-6203
TI  - Estimating ventilation rates in rooms with varying occupancy levels: Relevance for reducing transmission risk of airborne pathogens
T2  - PLoS One
UR  - http://dx.doi.org/10.1371/journal.pone.0253096
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34166388
UR  - http://hdl.handle.net/10044/1/90158
VL  - 16
ER  -
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