Prediction of COVID-19 infection risk using personal mobile location data only

Predicting an individuals risk of infectious disease is a critical technology in infectious disease response. During the COVID-19 pandemic, identifying and isolating individuals at high risk of infection was an essential task for epidemic control. We introduce a new machine learning model that predicts the risk of COVID-19 infection using only individuals mobile cell tower location information. This model distinguishes the cell tower location information of an individual into residential and non-residential areas and calculates whether the cell tower locations overlapped with other individuals. It then generates various variables from the information of overlapping and predicts the possibility of COVID-19 infection using a machine learning algorithm. The predictive model we developed showed performance comparable to models using individuals clinical information. This predictive model, which can be used to predict infections of diseases with asymptomatic infections such as COVID-19, has the advantage of supplementing the limitations of existing infectious disease prediction models that use symptoms and other information.