Progmosis:evaluating risky individual behavior during epidemics using mobile network data

Abstract

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%.

Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: D4D Senegal - NetMob 2015 Book of Abstracts: Posters
Event Title: NetMob 2015
Event Type: Other
Event Location: MIT Media Lab
Event Dates: 2015-04-07 - 2015-04-10
Full Text Link: http://arxiv.or ... 504.01316v1.pdf
Related URLs:
PURE Output Type: Poster
Published Date: 2015
Authors: Lima, A.
Pejovic, V.
Rossi, L. (ORCID Profile 0000-0002-6116-9761)
Musolesi, M.
Gonzalez, M.

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Version: Accepted Version


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