E. Frias-Martinez, G. Williamson, V. Frias-MartinezTelefonica Research, Madrid, Spainefm@tid.es

AN AGENT-BASED MODEL OF EPIDEMIC

SPREAD USING HUMAN MOBILITY AND SOCIAL

NETWORKS

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

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

1

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.

Call Detail Records

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URBAN 1-4km²

1

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|

Mobility Model

Social Network Model

Disease Model

ABM for Virus Spreading using CDR

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

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

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

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

Impact On Agents’ Mobility

April 27th May 1st May 6th

Alert Closed Shutdown Reopen

Intervention

Mobility reduced between 10% and 30%

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%

Spatio-Temporal Evolution

March 8thApril 29thMay 3rdMay 14th

Future

Enriched Agents (Gender, Age, Vaccinations)

Methodology for studying spatio-temporal evolution.