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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.accessioned2022-01-10T12:46:51Z
dc.date.available2022-01-10T12:46:51Z
dc.date.issued2021-12-31
dc.date.submitted2021-10-18
dc.identifier.others41564-021-01029-0
dc.identifier.other1029
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332502
dc.descriptionFunder: Oxford University | Jesus College, University of Oxford; doi: https://doi.org/10.13039/501100000645
dc.descriptionFunder: Joint Biosecurity Centre
dc.description.abstractAbstract: Global 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.languageen
dc.publisherNature Publishing Group UK
dc.subjectArticle
dc.subject/692/308/174
dc.subject/692/699/255/2514
dc.subjectarticle
dc.titleImproving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework
dc.typeArticle
dc.date.updated2022-01-10T12:46:50Z
prism.endingPage107
prism.issueIdentifier1
prism.publicationNameNature Microbiology
prism.startingPage97
prism.volume7
dc.identifier.doi10.17863/CAM.79952
dcterms.dateAccepted2021-11-18
rioxxterms.versionofrecord10.1038/s41564-021-01029-0
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://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-idRCUK | Medical Research Council (MRC) (MC_UP_A390_1107, MC_UU_00002/10, MR/S019669/1)
pubs.funder-project-idRCUK | Engineering and Physical Sciences Research Council (EPSRC) (EP/R018561/1, EP/R018561/1, EP/R018561/1)
pubs.funder-project-idWellcome Trust (Wellcome) (203141/Z/16/Z)
pubs.funder-project-idPublic Health England (PHE) (NIHR200915)
pubs.funder-project-idAlan Turing Institute (TU/B/000092)


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