Detailed Information
Cited 4 time in 
Cited 4 time in 
Cited 4 time in
Metadata Downloads
Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Sattler, Felix | - |
| dc.contributor.author | Ma, Jackie | - |
| dc.contributor.author | Wagner, Patrick | - |
| dc.contributor.author | Neumann, David | - |
| dc.contributor.author | Wenzel, Markus | - |
| dc.contributor.author | Schaefer, Ralf | - |
| dc.contributor.author | Samek, Wojciech | - |
| dc.contributor.author | Mueller, Klaus-Robert | - |
| dc.contributor.author | Wiegand, Thomas | - |
| dc.date.accessioned | 2021-08-30T11:34:30Z | - |
| dc.date.available | 2021-08-30T11:34:30Z | - |
| dc.date.created | 2021-06-18 | - |
| dc.date.issued | 2020-10-06 | - |
| dc.identifier.issn | 2398-6352 | - |
| dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/52490 | - |
| dc.description.abstract | Digital 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. | - |
| dc.language | English | - |
| dc.language.iso | en | - |
| dc.publisher | NATURE RESEARCH | - |
| dc.title | Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements | - |
| dc.type | Article | - |
| dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
| dc.identifier.doi | 10.1038/s41746-020-00340-0 | - |
| dc.identifier.scopusid | 2-s2.0-85092002356 | - |
| dc.identifier.wosid | 000582038500001 | - |
| dc.identifier.bibliographicCitation | NPJ DIGITAL MEDICINE, v.3, no.1 | - |
| dc.relation.isPartOf | NPJ DIGITAL MEDICINE | - |
| dc.citation.title | NPJ DIGITAL MEDICINE | - |
| dc.citation.volume | 3 | - |
| dc.citation.number | 1 | - |
| dc.type.rims | ART | - |
| dc.type.docType | Article | - |
| dc.description.journalClass | 1 | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Health Care Sciences & Services | - |
| dc.relation.journalResearchArea | Medical Informatics | - |
| dc.relation.journalWebOfScienceCategory | Health Care Sciences & Services | - |
| dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.