Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

This study develops a framework that integrates digital proxies of human mobility and physical mixing into conventional epidemic models to track COVID-19 transmissibility in near real-time and generate nowcasts and short-term forecasts. Using age-specific digital mobility data from Octopus cards in Hong Kong, the model accurately tracks the local effective reproduction number (Rt) of COVID-19, enabling quick assessment of intervention effectiveness. The findings demonstrate that integrating digital proxies into epidemic models provides accurate nowcasting and forecasting of COVID-19 epidemics.

Kathy Leung, Joseph T. Wu, Gabriel M. Leung