  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.
