  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%.
