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

A. Lima, V. Pejovic, L. Rossi, M. Musolesi, M. Gonzalez

Research output: Contribution to conference › Poster

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%.
Original languageEnglish
Pages16
Number of pages1
Publication statusPublished - 2015
EventNetMob 2015 - MIT Media Lab, Cambridge, MA, United States
Duration: 7 Apr 2015 → 10 Apr 2015

Conference

ConferenceNetMob 2015
CountryUnited States
CityCambridge, MA
Period7/04/15 → 10/04/15

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Wireless networks
Risk management
Viruses
Statistics
Costs

Bibliographical note

D4D Senegal - NetMob 2015

Book of Abstracts: Posters

Cite this

Lima, A., Pejovic, V., Rossi, L., Musolesi, M., & Gonzalez, M. (2015). Progmosis: evaluating risky individual behavior during epidemics using mobile network data. 16. Poster session presented at NetMob 2015, Cambridge, MA, United States.
Lima, A. ; Pejovic, V. ; Rossi, L. ; Musolesi, M. ; Gonzalez, M. / Progmosis : evaluating risky individual behavior during epidemics using mobile network data. Poster session presented at NetMob 2015, Cambridge, MA, United States.1 p.
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Lima, A, Pejovic, V, Rossi, L, Musolesi, M & Gonzalez, M 2015, 'Progmosis: evaluating risky individual behavior during epidemics using mobile network data' NetMob 2015, Cambridge, MA, United States, 7/04/15 - 10/04/15, pp. 16.

Progmosis : evaluating risky individual behavior during epidemics using mobile network data. / Lima, A.; Pejovic, V.; Rossi, L.; Musolesi, M.; Gonzalez, M.

2015. 16 Poster session presented at NetMob 2015, Cambridge, MA, United States.

Research output: Contribution to conference › Poster

TY - CONF

T1 - Progmosis

T2 - evaluating risky individual behavior during epidemics using mobile network data

AU - Lima, A.

AU - Pejovic, V.

AU - Rossi, L.

AU - Musolesi, M.

AU - Gonzalez, M.

N1 - D4D Senegal - NetMob 2015 Book of Abstracts: Posters

PY - 2015

Y1 - 2015

N2 - 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%.

AB - 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%.

M3 - Poster

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ER -

Lima A, Pejovic V, Rossi L, Musolesi M, Gonzalez M. Progmosis: evaluating risky individual behavior during epidemics using mobile network data. 2015. Poster session presented at NetMob 2015, Cambridge, MA, United States.

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