TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boosting
This addresses the need for efficient containment of COVID-19 spread by automating contact tracing, though it appears incremental as it applies existing methods to a new domain.
The researchers tackled the problem of automated contact tracing for COVID-19 by developing a machine learning system using sensor data from handheld devices, achieving effective prediction of close contacts with infected individuals through gradient boosting and time series feature extraction.
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.