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

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 e...

Descripción completa

Detalles Bibliográficos
Autores principales: Sattler, Felix, Ma, Jackie, Wagner, Patrick, Neumann, David, Wenzel, Markus, Schäfer, Ralf, Samek, Wojciech, Müller, Klaus-Robert, Wiegand, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538938/
https://www.ncbi.nlm.nih.gov/pubmed/33083564
http://dx.doi.org/10.1038/s41746-020-00340-0
Descripción
Sumario: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.

Ejemplares similares