In this paper we have proposed Progmosis, an approach to disease prevention and containment that goes beyond traditional epidemic modeling and contact tracing, and leverages behavioral data generated by mobile carrier networks to evaluate contagion risk on a per-user basis. The individual risk represents the loss incurred by not isolating or treating a specific person, both in terms of how likely it is for this person to spread the disease as well as how many secondary infections it will cause. We have developed and released an open-source tool that calculates this risk based on cellular network events. We have also simulated a realistic epidemic scenario, based on an Ebola virus outbreak. We have found that gradually restricting the mobility of a subset of individuals, selected using Progmosis greatly reduces the number of infected people, compared to a random choice.
This work focuses on a theoretical model and not on its actual translation into a real-world system. In particular, a decentralized deployment that preserves user privacy is to be preferred to a centralized one, which would require access to sensitive user data. While computer-based simulations show promising results, they are obtained under specific assumptions; real-world constraints and challenges might greatly affect the effectiveness of this model. It is worth remarking that simulations were performed using data of a country that is currently Ebola-free according to WHO. Finally, we would also stress the fact that this work has not been commissioned neither by Orange nor by any other organisation for preparation to a real-world disease outbreak.