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dc.contributor.authorChallenger, Joseph D
dc.contributor.authorFoo, Cher Y
dc.contributor.authorWu, Yue
dc.contributor.authorYan, Ada WC
dc.contributor.authorMarjaneh, Mahdi Moradi
dc.contributor.authorLiew, Felicity
dc.contributor.authorThwaites, Ryan S
dc.contributor.authorOkell, Lucy C
dc.contributor.authorCunnington, Aubrey J
dc.date.accessioned2022-02-14T02:02:26Z
dc.date.available2022-02-14T02:02:26Z
dc.date.issued2022-01-13
dc.identifier.issn1741-7015
dc.identifier.otherPMC8755404
dc.identifier.other35022051
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333986
dc.description.abstractRelationships between viral load, severity of illness, and transmissibility of virus are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies for COVID-19. Here we present within-host modelling of viral load dynamics observed in the upper respiratory tract (URT), drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic model to describe viral load dynamics and host response and contrast this with simpler mixed-effects regression analysis of peak viral load and its subsequent decline. We observed wide variation in URT viral load between individuals, over 5 orders of magnitude, at any given point in time since symptom onset. This variation was not explained by age, sex, or severity of illness, and these variables were not associated with the modelled early or late phases of immune-mediated control of viral load. We explored the application of the mechanistic model to identify measured immune responses associated with the control of the viral load. Neutralising antibodies correlated strongly with modelled immune-mediated control of viral load amongst subjects who produced neutralising antibodies. Our models can be used to identify host and viral factors which control URT viral load dynamics, informing future treatment and transmission blocking interventions.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 101190723
dc.sourceessn: 1741-7015
dc.subjectHumans
dc.subjectAntibodies, Viral
dc.subjectViral Load
dc.subjectCOVID-19
dc.subjectSARS-CoV-2
dc.titleModelling upper respiratory viral load dynamics of SARS-CoV-2.
dc.typeArticle
dc.date.updated2022-02-14T02:02:25Z
prism.issueIdentifier1
prism.publicationNameBMC Med
prism.volume20
dc.identifier.doi10.17863/CAM.81403
dcterms.dateAccepted2021-12-15
rioxxterms.versionofrecord10.1186/s12916-021-02220-0
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidChallenger, Joseph D [0000-0001-6029-1733]
dc.identifier.eissn1741-7015
pubs.funder-project-idimperial college london (Covid-19 Respond Fund)
pubs.funder-project-idMedical Research Council (MR/R015600/1, MR/V027409/1)
pubs.funder-project-idukri (MR/V027409/1)
cam.issuedOnline2022-01-13


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International