A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge

• URL: http://arxiv.org/abs/2203.04307v1
• Date: Tue, 8 Mar 2022 13:42:20 GMT
• Title: A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge
• Authors: Badrinath Singhal, Chris Vorster, Di Meng, Gargi Gupta, Laura Dunne and Mark Germaine
• Abstract summary: In this paper, we investigate the development of machine learning approaches to determine the distance between two mobile phone devices. We use Bluetooth Low Energy, sensory data and meta data to improve on the existing state of the art.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contact tracing is a method used by public health organisations to try prevent the spread of infectious diseases in the community. Traditionally performed by manual contact tracers, more recently the use of apps have been considered utilising phone sensor data to determine the distance between two phones. In this paper, we investigate the development of machine learning approaches to determine the distance between two mobile phone devices using Bluetooth Low Energy, sensory data and meta data. We use TableNet architecture and feature engineering to improve on the existing state of the art (total nDCF 0.21 vs 2.08), significantly outperforming existing models.

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