Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements

04/22/2020
by   Felix Sattler, et al.
13

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 novel 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.

READ FULL TEXT
research
07/19/2023

SecureTrack- A contact tracing IoT platform for monitoring infectious diseases

The COVID-19 pandemic has highlighted the need for innovative solutions ...
research
10/23/2020

Contact Tracing Made Un-relay-able

Automated contact tracing is a key solution to control the spread of air...
research
06/28/2021

Measuring close proximity interactions in summer camps during the COVID-19 pandemic

Policy makers have implemented multiple non-pharmaceutical strategies to...
research
09/26/2020

Proximity Inference with Wifi-Colocation during the COVID-19 Pandemic

In this work we propose a WiFi colocation methodology for digital contac...
research
07/20/2020

Inter-Mobile-Device Distance Estimation using Network Localization Algorithms for Digital Contact Logging Applications

Mobile applications are being developed for automated logging of contact...
research
03/31/2020

A Fully Distributed, Privacy Respecting Approach for Back-tracking of Potentially Infectious Contacts

In limiting the rapid spread of highly infectious diseases like Covid-19...
research
06/13/2022

Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readings

In this report, we present our solution to the challenge provided by the...

Please sign up or login with your details

Forgot password? Click here to reset