Improving epidemic risk maps using mobility information from mobile network data

In this paper we propose a method for using mobile network data to detect potential COVID-19 hospitalizations and derive corresponding epidemic risk maps. We apply our methods to a dataset from more than 2 million cellphones, collected by a mobile network provider located in London, UK. The approach yields a 98.6% agreement with released public records of patients admitted to NHS hospitals. Analyzing the mobility pattern of these individuals prior to their potential hospitalization, we present a series of risk maps. Compared with census-based maps, our risk maps indicate that the areas of highest risk are not necessarily the most densely populated ones and may change from day to day. Finally, we observe that hospitalized individuals tended to have a higher average mobility than non-hospitalized ones.