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dc.contributor.authorCastro Dopico, Xaquin
dc.contributor.authorMuschiol, Sandra
dc.contributor.authorGrinberg, Nastasiya F
dc.contributor.authorAleman, Soo
dc.contributor.authorSheward, Daniel J
dc.contributor.authorHanke, Leo
dc.contributor.authorAhl, Marcus
dc.contributor.authorVikström, Linnea
dc.contributor.authorForsell, Mattias
dc.contributor.authorCoquet, Jonathan M
dc.contributor.authorMcInerney, Gerald
dc.contributor.authorDillner, Joakim
dc.contributor.authorBogdanovic, Gordana
dc.contributor.authorMurrell, Ben
dc.contributor.authorAlbert, Jan
dc.contributor.authorWallace, Chris
dc.contributor.authorKarlsson Hedestam, Gunilla B
dc.date.accessioned2022-03-03T09:00:22Z
dc.date.available2022-03-03T09:00:22Z
dc.date.issued2022
dc.date.submitted2021-11-03
dc.identifier.issn2050-0068
dc.identifier.othercti21379
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334624
dc.description.abstractObjectives: Population-level measures of seropositivity are critical for understanding the epidemiology of an emerging pathogen, yet most antibody tests apply a strict cutoff for seropositivity that is not learnt in a data-driven manner, leading to uncertainty when classifying low-titer responses. To improve upon this, we evaluated cutoff-independent methods for their ability to assign likelihood of SARS-CoV-2 seropositivity to individual samples. Methods: Using robust ELISAs based on SARS-CoV-2 spike (S) and the receptor-binding domain (RBD), we profiled antibody responses in a group of SARS-CoV-2 PCR+ individuals (n = 138). Using these data, we trained probabilistic learners to assign likelihood of seropositivity to test samples of unknown serostatus (n = 5100), identifying a support vector machines-linear discriminant analysis learner (SVM-LDA) suited for this purpose. Results: In the training data from confirmed ancestral SARS-CoV-2 infections, 99% of participants had detectable anti-S and -RBD IgG in the circulation, with titers differing > 1000-fold between persons. In data of otherwise healthy individuals, 7.2% (n = 367) of samples were of uncertain serostatus, with values in the range of 3-6SD from the mean of pre-pandemic negative controls (n = 595). In contrast, SVM-LDA classified 6.4% (n = 328) of test samples as having a high likelihood (> 99% chance) of past infection, 4.5% (n = 230) to have a 50-99% likelihood, and 4.0% (n = 203) to have a 10-49% likelihood. As different probabilistic approaches were more consistent with each other than conventional SD-based methods, such tools allow for more statistically-sound seropositivity estimates in large cohorts. Conclusion: Probabilistic antibody testing frameworks can improve seropositivity estimates in populations with large titer variability.
dc.languageen
dc.publisherWiley
dc.subjectOriginal Article
dc.subjectantibody responses
dc.subjectantibody testing
dc.subjectCOVID‐19
dc.subjectprobability
dc.subjectSARS‐CoV‐2
dc.subjectserology
dc.titleProbabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.
dc.typeArticle
dc.date.updated2022-03-03T09:00:21Z
prism.issueIdentifier3
prism.publicationNameClin Transl Immunology
prism.volume11
dc.identifier.doi10.17863/CAM.82043
dcterms.dateAccepted2022-02-17
rioxxterms.versionofrecord10.1002/cti2.1379
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidCastro Dopico, Xaquin [0000-0002-9005-6774]
dc.contributor.orcidGrinberg, Nastasiya F [0000-0002-2727-5130]
dc.contributor.orcidAleman, Soo [0000-0003-0461-4870]
dc.contributor.orcidSheward, Daniel J [0000-0002-0227-5636]
dc.contributor.orcidHanke, Leo [0000-0001-5514-2418]
dc.contributor.orcidForsell, Mattias [0000-0001-6904-742X]
dc.contributor.orcidCoquet, Jonathan M [0000-0002-5967-4857]
dc.contributor.orcidMcInerney, Gerald [0000-0003-2257-7241]
dc.contributor.orcidDillner, Joakim [0000-0001-8588-6506]
dc.contributor.orcidBogdanovic, Gordana [0000-0003-1563-8640]
dc.contributor.orcidMurrell, Ben [0000-0002-0393-4445]
dc.contributor.orcidAlbert, Jan [0000-0001-9020-0521]
dc.contributor.orcidWallace, Chris [0000-0001-9755-1703]
dc.contributor.orcidKarlsson Hedestam, Gunilla B [0000-0001-7255-9047]
dc.identifier.eissn2050-0068
pubs.funder-project-idWellcome Trust (107881/Z/15/Z)
pubs.funder-project-idNational Institute for Health Research (NIHRDH-IS-BRC-1215-20014)
cam.issuedOnline2022-03-02


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