# A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge

arXiv: https://arxiv.org/abs/2203.04307 · HTML: https://mlsanity.com/paper/2203.04307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images](https://mlsanity.com/paper/2001.01469/) (2020)
- [A Simplistic Machine Learning Approach to Contact Tracing](https://mlsanity.com/paper/2012.05940/) (2020)
- [Coronavirus Contact Tracing: Evaluating The Potential Of Using Bluetooth Received Signal Strength For Proximity Detection](https://mlsanity.com/paper/2006.06822/) (2020)
- [Using Bluetooth Low Energy (BLE) Signal Strength Estimation to Facilitate Contact Tracing for COVID-19](https://mlsanity.com/paper/2006.15711/) (2020)

## Abstract (arXiv)

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.