Abstract
Original language | English |
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Pages | 16 |
Number of pages | 1 |
Publication status | Published - 2015 |
Event | NetMob 2015 - MIT Media Lab, Cambridge, MA, United States Duration: 7 Apr 2015 → 10 Apr 2015 |
Conference
Conference | NetMob 2015 |
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Country | United States |
City | Cambridge, MA |
Period | 7/04/15 → 10/04/15 |
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Bibliographical note
D4D Senegal - NetMob 2015Book of Abstracts: Posters
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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
SP - 16
ER -