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dc.date.accessioned2022-04-06T15:42:12Z
dc.date.available2022-04-06T15:42:12Z
dc.date.created2021-09-21T14:23:09Z
dc.date.issued2021
dc.identifier.citationElmokashfi, Ahmed Mustafa Abdalla Sundnes, Joakim Kvalbein, Amund Naumova, Valeriya Reinemo, Sven-Arne Florvaag, Per Magne Stensland, Håkon Kvale Lysne, Olav . Nationwide rollout reveals efficacy of epidemic control through digital contact tracing. Nature Communications. 2021, 12, 1-8
dc.identifier.urihttp://hdl.handle.net/10852/93395
dc.description.abstractFuelled by epidemiological studies of SARS-CoV-2, contact tracing by mobile phones has been put to use in many countries. Over a year into the pandemic, we lack conclusive evidence on its effectiveness. To address this gap, we used a unique real world contact data set, collected during the rollout of the first Norwegian contact tracing app in the Spring of 2020. Our dataset involves millions of contacts between 12.5% of the adult population, which enabled us to measure the real-world app performance. The technological tracing efficacy was measured at 80%, and we estimated that at least 11.0% of the discovered close contacts could not have been identified by manual contact tracing. Our results also indicated that digital contact tracing can flag individuals with excessive contacts, which can help contain superspreading related outbreaks. The overall effectiveness of digital tracing depends strongly on app uptake, but significant impact can be achieved for moderate uptake numbers. Used as a supplement to manual tracing and other measures, digital tracing can be instrumental in controlling the pandemic. Our findings can thus help informing public health policies in the coming months.
dc.languageEN
dc.publisherNature Portfolio
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleNationwide rollout reveals efficacy of epidemic control through digital contact tracing
dc.typeJournal article
dc.creator.authorElmokashfi, Ahmed Mustafa Abdalla
dc.creator.authorSundnes, Joakim
dc.creator.authorKvalbein, Amund
dc.creator.authorNaumova, Valeriya
dc.creator.authorReinemo, Sven-Arne
dc.creator.authorFlorvaag, Per Magne
dc.creator.authorStensland, Håkon Kvale
dc.creator.authorLysne, Olav
cristin.unitcode185,15,5,45
cristin.unitnameML Maskinlæring
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.cristin1936627
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Nature Communications&rft.volume=12&rft.spage=1&rft.date=2021
dc.identifier.jtitleNature Communications
dc.identifier.volume12
dc.identifier.issue1
dc.identifier.doihttps://doi.org/10.1038/s41467-021-26144-8
dc.identifier.urnURN:NBN:no-95965
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn2041-1723
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/93395/4/s41467-021-26144-8.pdf
dc.type.versionPublishedVersion
cristin.articleid5918


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