Exploiting Cellular Data for Disease Containment and Information Campaigns Strategies in Country-wide Epidemics

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1 Exploiting Cellular Data for Disease Containment and Information Campaigns Strategies in Country-wide Epidemics Antonio Lima, Manlio De Domenico, Veljko Pejovic, Mirco Musolesi Multi-Service Networks The Cosener s House, Abingdon, England July 12, 2013

2 Applications for Cellular Data Analysis Providers are not merely service suppliers. They collect usage data, they analyse them to get insights about about user needs. Obvious applications: infrastructure optimisation, marketing. Analysis of data coming from a large number of people can support decision-making in critical scenarios.

3 Data 4 Development Challenge Data from 5+ Orange mobile phones in Ivory Coast. Strong research interest: 250+ groups participated to the challenge. Challenge: help address society development questions in novel ways.

4 Developing Countries Healthcare is one of the most urgent issues. Difficulties in gathering upto-date data for healthcare decision-making. Mobile phones can provide access to real-time data on human behavior at country level.

5 Epidemics Containment In this work, we focus on containment of epidemics. We develop an epidemic model on a network of metapopulations, evaluating the impact of mobility. We extend the model to include a competing process of information spreading that helps to contain diseases.

6 D4D Challenge Data Data about 5M+ users spanning over 5 months. Aggregated number of calls per hour between towers. A sample of 50,000 mobility traces, for five months. Other fine-grained data about mobility and calls.

7 Mobility Network Call Network Mobility Network and Arcs range from yellow (origin) to red (destination). Call Network

8 Mobility and Information Spreading From the two datasets, we build two matrices. Mobility matrix, representing the probability that a person in subprefecture i moves to subprefecture j. Calls matrix, representing the probability that a person in subprefecture i calls a person in subprefecture j Mobility matrix M Calls matrix C

9 The SIS model Recovery Contagion One metapopulation, 2 differential equations. Each person is described by a state: S susceptible or I infected.

10 Enhancing the SIS with mobility Our model describes N metapopulations => 2N differential equations. We extend the SIS model using the mobility matrix. For each district i we can write the following state equations:

11 Enhancing the SIS with mobility Our model describes N metapopulations => 2N differential equations. We extend the SIS model using the mobility matrix. For each district i we can write the following state equations: Mobility matrix

12 Extending the Model with Information Spreading Immunization Recovery De-Immunization Contagion Information spreading Each person is described by a two-dimensional state. A disease state dimension: R resistant or S susceptible or I infected. An information state dimension: U unaware or A aware.

13 Disease and Information Spreading 5N state equations describe the system: Distance information "contagion"

14 Analysis We initialize each scenario by: Allocating 22M people according to the geographic distribution found in the mobility traces. Infecting a fixed amount of population (0.1%), distributed across the metapopulations according to different criteria (uniform, random, most central metapopulations). A series of Monte-Carlo simulations for multiple sets of parameters is in accordance with the analytical model presented, confirming its validity.

15 Simulations We investigate two measures: fraction of infected population at the stationary state i 1 time required to reach the stationary state In particular, then study three groups of scenarios: no countermeasures taken; geographic quarantine: we block all the people going in and out a certain number of regions; information campaign (social immunization).

16 No countermeasures i uniform top 10 top 5 top 1 random uniform top 10 top 5 top 1 random r Fraction of infected population at the stationary state (left) and time required to reach it (right), for different initial conditions. r 0 On the x axis the basic reproductive ratio r 0 = of a classic SIS model.

17 Geographic quarantine not curbed curbed top1 curbed top5 curbed top not curbed curbed top1 curbed top5 curbed top10 i r Fraction of infected population at the stationary state (left) and time required to reach it (right). The values are averaged over all possible initial starting regions. r 0 On the x axis the basic reproductive ratio r 0 = of a classic SIS model.

18 Information campaign We now investigate how an information campaign can contrast the spread of the disease. We distribute 1% of the immunizing information to the population, randomly chosen regardless of their position. These people will be informed and will be instructed to spread the information, contacting their social contacts according to the call matrix.

19 Information campaign! = i 1 ( =0.8, =0.4) time to reach the stationary state ( = 0.8, = 0.4)! =! = ( =0.8, =0.4) > > Fraction of infected population at the stationary state (left) and time required to reach it (right) with a basic reproductive ratio = 2.

20 flickr: rogermeyer Take-away Messages The analysis of large-scale datasets can support decision-making. Restricting mobility does not delay the occurrence of an endemic state. An information campaign based on word-of-mouth, exploiting a collaborative effort of the population, can contrast the disease.

21 flickr: johnkarakatsanis Thanks! Questions? Antonio Lima Thanks to flickr authors of CC-licenced images.