Measuring the impact ofepidemic alerts on humanmobility using cell-phone      network data       Enrique Frias-Martinez Te...
   Human mobility plays a central role in the spatial    spreading of infectious diseases.   WHO recommends suspension o...
   In this paper we measure the impact that the    actions taken by the Mexican government during    April and May of 200...
Closed                  ReopenPreflu                27th April              6th May          Alert                    Shut...
Digital FootprintsFor the first time in human history, we have access to large-scale human behavioral data   at varying le...
2233445566|3E884DB|15/02/2011|23:02:35|...               2233445567|3E884DC|16/02/2011|23:02:35|...               22334455...
   CDRs for 1,000,000 anonymized customers from    one of the most affected Mexican cities were    obtained for a period ...
   Diameter of Influence
   Definition of baselines:       baseline 1 - quantifies changes in mobility during Medical Alert        (from Thursday...
   Distribution of the Diameter of Influence for each    baseline and its corresponding alert period.   Lilliefors test ...
   Stage 1 alert period: no significant change in    human mobility.   Stage 2 and Stage 3: t-test revealed statisticall...
   Quantify the changes in mobility during alert    periods 2 and 3.       CDF of diameter change for April 27th relativ...
   Evaluates impact that alerts had on critical    infrastructures.   Understand whether the number of individuals    th...
   University Campus
   Airport
   Lilliefors indicates normal distribution of hourly    representation – Not-normal for hourly representation.   Daily ...
   Use of CDR for qualitative & quantitative evaluating the    impact of government mandates.   Reduction in mobility is...
Thanks Mexico! "An Agent-Based Model of Epidemic Spread using Human Mobility andSocial Network Information", The 3rd IEEE ...
Measuring the impact of epidemic alerts on human mobility using cell-phone network data
Measuring the impact of epidemic alerts on human mobility using cell-phone network data
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Measuring the impact of epidemic alerts on human mobility using cell-phone network data

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Accumulating evidence reveals a strong link between human
mobility and the spread of epidemics. In order to control the spread of an
epidemic, governments can implement mobility restrictions to its citizens. The
effect of such restrictions on the mobility of the population has not been
adequately studied at a large scale mainly due to the lack of relevant data.
Nevertheless, the recent adoption of ubiquitous computing technologies enables
the design of such studies. In this paper we measure the impact that the alerts
issued by the Mexican government had on the mobility of the Mexican
population during the H1N1 flu outbreak in April and May of 2009. The
mobility of individuals was characterized using anonymized Call Detail
Records (CDRs) traces. The results indicate a statistically significant reduction,
of up to 80% in some cases, in the diameter of mobility of individuals.

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  • As opposed to economic costs
  • Measuring the impact of epidemic alerts on human mobility using cell-phone network data

    1. 1. Measuring the impact ofepidemic alerts on humanmobility using cell-phone network data Enrique Frias-Martinez Telefonica Research, Madrid, Spain efm@tid.es
    2. 2.  Human mobility plays a central role in the spatial spreading of infectious diseases. WHO recommends suspension of activities as a measure to reduce the transmission of a disease. Actions implemented by the Mexican government to control the H1N1 flu outbreak of April 2009 constitute an illustrative example.
    3. 3.  In this paper we measure the impact that the actions taken by the Mexican government during April and May of 2009 had on human mobility using phone Call Detail Records (CDRs). We evaluate the impact of the alerts using two approaches:  Population Mobility Analysis  Geographic Mobility Analysis
    4. 4. Closed ReopenPreflu 27th April 6th May Alert Shutdown 17th April 1st May Medical Alert (Stage 1)-> Closing of Schools (Stage 2)-> Suspension (Stage 3)
    5. 5. Digital FootprintsFor the first time in human history, we have access to large-scale human behavioral data at varying levels of spatial and temporal granularities
    6. 6. 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
    7. 7.  CDRs for 1,000,000 anonymized customers from one of the most affected Mexican cities were obtained for a period of 5 months from January 2009 to May 2009. Only considered users with an average of two daily calls or more (20% population).
    8. 8.  Diameter of Influence
    9. 9.  Definition of baselines:  baseline 1 - quantifies changes in mobility during Medical Alert (from Thursday 16th to Wednesday 22nd). Computed using mobility data from four 7-day time periods – from Thursday to Wednesday – prior to April 16th.  baseline 2 - defined to evaluate changes in mobility during stage 2 (from Monday 27th to Thursday 30th). Computed using four 4- day time periods – from Monday to Thursday –prior to April 16th.  baseline 3- defined to identify mobility changes during the stage 3 alert (Friday May 1st to Tuesday May 5th), was computed using data from Easter holidays.
    10. 10.  Distribution of the Diameter of Influence for each baseline and its corresponding alert period. Lilliefors test indicates normal distribution. In order to quantify the impact of the government calls on mobility, a one-sided t-test (p<0.01) was used to compare the distribution of diameters between each stage and its corresponding baseline.
    11. 11.  Stage 1 alert period: no significant change in human mobility. Stage 2 and Stage 3: t-test revealed statistically significant differences with p<0.01:  the distribution of the diameter of mobility was reduced during these two stages compared to their baselines.
    12. 12.  Quantify the changes in mobility during alert periods 2 and 3.  CDF of diameter change for April 27th relative to its baseline - 80% of the population reduced its diameter - around 50% of them by 20km or more.
    13. 13.  Evaluates impact that alerts had on critical infrastructures. Understand whether the number of individuals that visited these infrastructures changed.  University Campus  Hospital  Airport BTS Coverage Area / Correction with Home algorithm
    14. 14.  University Campus
    15. 15.  Airport
    16. 16.  Lilliefors indicates normal distribution of hourly representation – Not-normal for hourly representation. Daily representation: t-test  Hospital: No change in any period.  University Campus: Reduction number of visitors in Stage 2 and Stage 3.  Airport: statistically significant increase in the number of visitors to the airport during the same stage 2 alert period. No change period 1 or 3. Hourly representation: Kolgomorov-Smirnof test and a Wilcoxon rank-sum test  No statistical differences at hourly level
    17. 17.  Use of CDR for qualitative & quantitative evaluating the impact of government mandates. Reduction in mobility is higher when schools are closed during regular days (stage 2) than when all non-essential activities are closed (stage 3) during a period that already was a holiday. Total closing of infrastructures (stage 3 alert) might provoke an increase in the number of individuals that visit transport hubs before its enactment. No statistical significant change in the number of visitors to the Hospital
    18. 18. Thanks Mexico! "An Agent-Based Model of Epidemic Spread using Human Mobility andSocial Network Information", The 3rd IEEE Int. Conf. on Social Computing (SocialCom 2011), Boston, MA, USA efm@tid.es www.enriquefrias-martinez.info

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