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dc.contributor.authorBurridge, HC
dc.contributor.authorFan, S
dc.contributor.authorJones, RL
dc.contributor.authorNoakes, CJ
dc.contributor.authorLinden, PF
dc.date.accessioned2022-05-21T18:00:16Z
dc.date.available2022-05-21T18:00:16Z
dc.date.issued2022
dc.identifier.issn1420-326X
dc.identifier.other10.1177_1420326x211043564
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337367
dc.description.abstract<jats:p> The risk of long range, herein ‘airborne', infection needs to be better understood and is especially urgent during the COVID-19 pandemic. We present a method to determine the relative risk of airborne transmission that can be readily deployed with either modelled or monitored CO<jats:sub>2</jats:sub> data and occupancy levels within an indoor space. For spaces regularly, or consistently, occupied by the same group of people, e.g. an open-plan office or a school classroom, we establish protocols to assess the absolute risk of airborne infection of this regular attendance at work or school. We present a methodology to easily calculate the expected number of secondary infections arising from a regular attendee becoming infectious and remaining pre/asymptomatic within these spaces. We demonstrate our model by calculating risks for both a modelled open-plan office and by using monitored data recorded within a small naturally ventilated office. In addition, by inferring ventilation rates from monitored CO<jats:sub>2</jats:sub>, we show that estimates of airborne infection can be accurately reconstructed, thereby offering scope for more informed retrospective modelling should outbreaks occur in spaces where CO<jats:sub>2</jats:sub> is monitored. Well-ventilated spaces appear unlikely to contribute significantly to airborne infection. However, even moderate changes to the conditions within the office, or new variants of the disease, typically result in more troubling predictions. </jats:p>
dc.languageen
dc.publisherSAGE Publications
dc.subjectSpecial Issue - Prevention and control of COVID-19 transmission in the indoor environment
dc.subjectInfection modelling
dc.subjectAirborne infection risk
dc.subjectMonitored CO2
dc.subjectCOVID-19
dc.subjectCoronavirus SAR-CoV-2
dc.titlePredictive and retrospective modelling of airborne infection risk using monitored carbon dioxide
dc.typeArticle
dc.date.updated2022-05-21T18:00:16Z
prism.endingPage1380
prism.issueIdentifier5
prism.publicationNameIndoor and Built Environment
prism.startingPage1363
prism.volume31
dc.identifier.doi10.17863/CAM.84781
dcterms.dateAccepted2021-08-15
rioxxterms.versionofrecord10.1177/1420326X211043564
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
rioxxterms.licenseref.startdate2021-09-28
dc.contributor.orcidBurridge, HC [0000-0002-0719-355X]
dc.identifier.eissn1423-0070
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N010221/1)
pubs.funder-project-idEPSRC (EP/W001411/1)
pubs.funder-project-idNERC (NE/V002341/1)
cam.issuedOnline2021-09-28
rioxxterms.freetoread.startdate2021-09-28
rioxxterms.freetoread.startdate2021-09-28


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