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dc.contributor.authorNicholson, George
dc.contributor.authorLehmann, Brieuc
dc.contributor.authorPadellini, Tullia
dc.contributor.authorPouwels, Koen B
dc.contributor.authorJersakova, Radka
dc.contributor.authorLomax, James
dc.contributor.authorKing, Ruairidh E
dc.contributor.authorMallon, Ann-Marie
dc.contributor.authorDiggle, Peter J
dc.contributor.authorRichardson, Sylvia
dc.contributor.authorBlangiardo, Marta
dc.contributor.authorHolmes, Chris
dc.date.accessioned2022-02-02T01:49:18Z
dc.date.available2022-02-02T01:49:18Z
dc.date.issued2022-01
dc.identifier.issn2058-5276
dc.identifier.other34972825
dc.identifier.otherPMC8727294
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333546
dc.descriptionFunder: Oxford University | Jesus College, University of Oxford
dc.descriptionFunder: Joint Biosecurity Centre
dc.description.abstractGlobal and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.
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: 101674869
dc.sourceessn: 2058-5276
dc.subjectHumans
dc.subjectPrevalence
dc.subjectModels, Statistical
dc.subjectReproducibility of Results
dc.subjectForecasting
dc.subjectBasic Reproduction Number
dc.subjectSpatio-Temporal Analysis
dc.subjectUnited Kingdom
dc.subjectBias
dc.subjectCOVID-19
dc.subjectSARS-CoV-2
dc.subjectCOVID-19 Testing
dc.titleImproving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework.
dc.typeArticle
dc.date.updated2022-02-02T01:49:17Z
prism.endingPage107
prism.issueIdentifier1
prism.publicationNameNat Microbiol
prism.startingPage97
prism.volume7
dc.identifier.doi10.17863/CAM.80966
dcterms.dateAccepted2021-11-18
rioxxterms.versionofrecord10.1038/s41564-021-01029-0
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidNicholson, George [0000-0001-9588-6075]
dc.contributor.orcidLehmann, Brieuc [0000-0002-7302-4391]
dc.contributor.orcidPouwels, Koen B [0000-0001-7097-8950]
dc.contributor.orcidKing, Ruairidh E [0000-0001-6733-8805]
dc.contributor.orcidRichardson, Sylvia [0000-0003-1998-492X]
dc.identifier.eissn2058-5276
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R018561/1)
cam.issuedOnline2021-12-31


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