TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boosting

TL;DR

This paper introduces TsFeX, an automated contact tracing model that leverages time series feature extraction and gradient boosting to predict close contact with COVID-19 infected individuals using sensor data.

Contribution

The paper presents a novel machine learning approach combining time series feature extraction with gradient boosting for contact tracing.

Findings

Effective prediction of close contact using sensor data

Improved accuracy over manual contact tracing methods

Automated system suitable for real-time deployment

Abstract

With the outbreak of COVID-19 pandemic, a dire need to effectively identify the individuals who may have come in close-contact to others who have been infected with COVID-19 has risen. This process of identifying individuals, also termed as 'Contact tracing', has significant implications for the containment and control of the spread of this virus. However, manual tracing has proven to be ineffective calling for automated contact tracing approaches. As such, this research presents an automated machine learning system for identifying individuals who may have come in contact with others infected with COVID-19 using sensor data transmitted through handheld devices. This paper describes the different approaches followed in arriving at an optimal solution model that effectually predicts whether a person has been in close proximity to an infected individual using a gradient boosting algorithm and time series feature extraction.