Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements

dc.contributor.authorSattler, Felix
dc.contributor.authorMa, Jackie
dc.contributor.authorWagner, Patrick
dc.contributor.authorNeumann, David
dc.contributor.authorWenzel, Markus
dc.contributor.authorSchäfer, Ralf
dc.contributor.authorSamek, Wojciech
dc.contributor.authorMüller, Klaus-Robert
dc.contributor.authorWiegand, Thomas
dc.date.accessioned2023-08-09T15:46:46Z
dc.date.available2023-08-09T15:46:46Z
dc.date.issued2020-10-06
dc.date.updated2023-06-15T13:07:07Z
dc.description.abstractDigital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19.en
dc.description.sponsorshipDFG, 390685689, EXC 2046: MATH+: Berlin Mathematics Research Center
dc.description.sponsorshipBMBF, 01IS18025A, BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Data
dc.identifier.eissn2398-6352
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/19238
dc.identifier.urihttps://doi.org/10.14279/depositonce-18034
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc600 Technik, Medizin, angewandte Wissenschaften::600 Technik::600 Technik, Technologie
dc.subject.othercomputer scienceen
dc.subject.otherrisk factorsen
dc.subject.otherviral infectionen
dc.titleRisk estimation of SARS-CoV-2 transmission from bluetooth low energy measurementsen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber129
dcterms.bibliographicCitation.doi10.1038/s41746-020-00340-0
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitlenpj Digital Medicine
dcterms.bibliographicCitation.originalpublishernameSpringer Nature
dcterms.bibliographicCitation.originalpublisherplaceHeidelberg
dcterms.bibliographicCitation.volume3
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernen
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Telekommunikationssysteme::FG Medientechnik
tub.publisher.universityorinstitutionTechnische Universität Berlin

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