Using mobile network data to color epidemic risk maps.

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 over the months of March and April in 2020 by a British mobile network provider. The method consists of different algorithms, including detection, filtering, validation and fine-tuning. The approach detected over 2,800 potentially hospitalized individuals, yielding 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. We also show that the areas of highest risk may change from day to day. Finally, we observe that hospitalized individuals tended to have a higher average mobility than non-hospitalized ones. Overall, we conclude that the rich spatio-temporal information extracted from mobile network data may benefit both the mobile-based technologies and the policies that are being developed against existing and future epidemics.