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With the outbreak of COVID-19 pandemic, a dire need to effectively identifythe individuals who may have come in close-contact to others who have beeninfected with COVID-19 has risen. This process of identifying individuals, alsotermed as 'Contact tracing', has significant implications for the containmentand control of the spread of this virus. However, manual tracing has proven tobe ineffective calling for automated contact tracing approaches. As such, thisresearch presents an automated machine learning system for identifyingindividuals who may have come in contact with others infected with COVID-19using sensor data transmitted through handheld devices. This paper describesthe different approaches followed in arriving at an optimal solution model thateffectually predicts whether a person has been in close proximity to aninfected individual using a gradient boosting algorithm and time series featureextraction.

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