Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 33083564.
- Also identified by DOI 10.1038/s41746-020-00340-0 and PMC identifier 7538938.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.