Progmosis: Evaluating Risky Individual Behavior During Epidemics Using Mobile Network Data

TL;DR

Progmosis introduces a novel method using mobile network data to assess individual contagion risk during epidemics, enabling targeted interventions that can significantly reduce infection spread.

Contribution

This paper presents Progmosis, an innovative model and open-source tool that quantifies individual contagion risk using mobile data, surpassing traditional modeling and contact tracing methods.

Findings

Restricting mobility of high-risk individuals reduces infections by 24% after 30 days.

The model effectively identifies individuals with higher contagion potential.

Simulation based on Ebola demonstrates practical utility of the approach.

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%.