A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge
This work addresses a domain-specific problem for public health organizations by improving contact tracing accuracy, though it appears incremental as it builds on existing methods like TableNet.
The paper tackled the problem of determining distance between mobile phones for digital contact tracing using Bluetooth Low Energy and sensor data, achieving a total nDCF of 0.21 compared to 2.08 for existing models, significantly outperforming prior approaches.
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.