The possibility to analyze, quantify and forecast epidemic outbreaks is fundamental when devising effective disease containment strategies. Policy makers are faced with the intricate task of drafting realistically implementable policies that strike a balance between risk management and cost. Two major techniques policy makers have at their disposal are: epidemic modeling and contact tracing. Models are used to forecast the evolution of the epidemic both globally and regionally, while contact tracing is used to reconstruct the chain of people who have been potentially infected, so that they can be tested, isolated and treated immediately. However, both techniques might provide limited information, especially during an already advanced crisis when the need for action is urgent.
In this paper we propose an alternative approach that goes beyond 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. To this aim, we develop a model, named Progmosis, which quantifies this risk based on movement and regional aggregated statistics about infection rates. We develop and release an open-source tool that calculates this risk based on cellular network events. We simulate a realistic epidemic scenarios, based on an Ebola virus outbreak; we find that gradually restricting the mobility of a subset of individuals reduces the number of infected people after 30 days by 24%.
While these results are promising, it is important to underline the fact that this is only an initial foundational work and to stress some key points. First, this paper focuses on a theoretical model, rather than on its actual translation into a real-world system. In particular, centralized deployments of this model would pose several ethical questions, as they would require access to user data. Decentralized deployments for which user mobility data never leaves the mobile device of a user are possible and should be preferred, as they fully protect user privacy. Second, results are generated from computer-based simulations, under specific assumptions. Social factors and technical difficulties might greatly affect results obtained in the real world. Third, this risk-assessment tool is not designed specifically for implementing containment measures based on mobility restrictions. For example, it could be used to advise users about the most appropriate behavior given his/her risk profile (e.g., willingly change own behavior, see a doctor, and similar); users would finally choose whether to follow the advice or not. Finally, the simulations were run on data call records from a country that is according to WHO Ebola-free \cite{who_senegal_2014}, and this work has not been commissioned neither by Orange nor by any other entity for preparation to a real-world disease outbreak.