An Agent-Based Model of Epidemic Spread using Human Mobility and Social Network Information
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E. Frias-Martinez, G. Williamson, V. Frias-MartinezTelefonica Research, Madrid, [email protected]
AN AGENT-BASED MODEL OF EPIDEMIC
SPREAD USING HUMAN MOBILITY AND SOCIAL
NETWORKS
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Susceptible
Exposed
Infectious
RecoveredContact
RateTransition Rate
Recovery Rate
Epidemic Disease Models Compartmental Models (SEIR)
Agent Based Models Capure complexity of social interaction Limitation with the information available to generate the
agents
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Unprecedented Historic Moment
Digital Footprints For the first time in human history, we have
access to large-scale human behavioral data at varying levels of spatial and temporal
granularities
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Cell Phone Network
Cell Phone networks are built using Base Transceiver Stations (BTS).
Each BTS will be characterized by a feature vector that describes the calling behavior area.
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Call Detail Records
2233445566|3E884DB|15/02/2011|23:02:35|...2233445567|3E884DC|16/02/2011|23:02:35|...2233445568|3E884DD|17/02/2011|23:02:35|...2233445569|3E884E5|18/02/2011|23:02:35|...
URBAN 1-4km²
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CDR dataset
Our Dataset
•1 month of phone call interactions.
•1100 Base Transceiver Stations.
•Each CDR contains:
› phoneSource | phoneDestiny | btsSource | btsDestiny | DD/MM/YYYY | hh:mm:ss | d
• Phone number are encrypted to anonymize user identities.
Traffic
Subscribers sample
Cell catalogue
Mobility algorithms
2233445566|15/02/2008|2233445567|15/01/2008|2233445568|15/07/2008|25/07/20102233445569|15/09/2008|
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Mobility Model
Social Network Model
Disease Model
ABM for Virus Spreading using CDR
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Discrete Event Simulator
Mobility ModelSocial Network
Model Disease Model
t t t t … t (1 hour) ₀ ₁ ₂ ₃ ₉
Identify geographical location (BTS)Identify peers in same BTS If peer in SN then evolve disease model with p_i Else evolve disease model with p_j
M1M2
M3S1
S2S3
D1D2
D3
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H1N1 Mexico Timeline
PrefluClosed27th April
Reopen6th May
Measure the impact that government alerts had on the population
Flu is very good candidate to be modelled by SEIR
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Measuring Impact in Mexico
Call Detail Records from 1st Jan. till 31st.May 2009
Compute mobility and social models Baseline scenario Intervention scenario Simulation April 17th to May 16th
“Evolve” disease and evaluate impact in Agent’s mobility Disease transmission Spatio-temporal evolution
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Call Detail Records from 1st Jan. till 31st.May 2009
Granularity of 1 hour 20% of slots filled /0.25 calls per hour Agents active during the different time periods Final number of agents: 25,000 Reproduction number / Latent period / infectious
period obtained from the literature.
Agent Generation
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Impact On Agents’ Mobility
April 27th May 1st May 6th
Alert Closed Shutdown Reopen
Intervention
Mobility reduced between 10% and 30%
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Impact on Disease Propagation
Baseline (“preflu” behavior all weeks)Intervention (alert,closed,shutdown)
Epidemic peak postponed 40 hours
Reduced number of infected in peak agents by 10%
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Spatio-Temporal Evolution
March 8thApril 29thMay 3rdMay 14th
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Future
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Enriched Agents (Gender, Age, Vaccinations)
Methodology for studying spatio-temporal evolution.