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Measuring a Human Contact Network for Epidemiology Research

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Measuring a Human Contact Network for Epidemiology Research Maria Kazandjieva, Jung Woo Lee, Marcel Salathé Marcus Feldman, James Jones, Philip Levis Stanford University HotEmNets 2010
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Page 1: Measuring a Human Contact Network for Epidemiology Research

Measuring a Human Contact Network for Epidemiology Research

Maria Kazandjieva, Jung Woo Lee, Marcel Salathé Marcus Feldman, James Jones, Philip Levis

Stanford University

HotEmNets 2010

Page 2: Measuring a Human Contact Network for Epidemiology Research

Contact Networks

Specify physical proximity interactions over time

Used by National Institute of Health Center for Disease Control

Key in determining how to limit disease spread e.g. vaccinations

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Epidemiology’s Problem

Not enough data!

Pen-and-paper and survey methods are inaccurate have limited coverage

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The Alternative

Use technology to get more fine grained and accurate data

Previous work has used cell phones and RFID on small populations

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The Deployment

8-hour day at a U.S. high school - closed network environment

850 participants - students, teachers, staff

5+ school buildings 170 location nodes

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the motes

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The Code

Mote type Number Beacon interval TX Power participant 850 20 sec -16.9 dBm location 170 20 sec -11 dBm

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Over 1000 motes broadcasting periodic beacons

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Data Format

source bcn_seqno rssi local_seqno

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Preparation

Program 1000+ motes

Place 170 location motes the night before

Prepare batches of 10 to 15 motes for teachers to distribute to students

Brief the school 9

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D-Day

In at 6:30 am, out by 9 pm

Distribute motes, pouches, and assent forms

Participants write down the time and start the mote by inserting the second battery

4 pm - motes start to come back 10

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The Rest of This Talk

Pre-deployment considerations working with epidemiologists working with human subjects

Post-deployment woes node resets and disconnections

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Epidemiology Considerations

Trade-off between time resolution of data and coverage

20 seconds is good enough

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Opting in and out user button vs. batteries

Indicate state of the mote red vs. yellow LED

Human Subject Considerations

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The Deployment

8-hour day at a U.S. high school

850 participants

5+ school buildings

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D-Day Outcome

792 traces from participants

Total of 3 million contact entries

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But During the Deployment …

Batteries fell out Batteries were taken out Students played with the reset button Motes were accidentally bumped against desks Students banged motes against hard surfaces Students swung motes by the lanyards Motes were rubbed together … to see what happens

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Node reboots

272 nodes with uninterrupted data traces a.k.a. ‘safe motes’

520 nodes with a total of over 1500 reboots

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Hardware Resets

source bcn_seqno rssi local_seqno

450 300 220 370 789 414 223 370 450 301 221 371 450 302 220 0

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Extended Disconnection

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80 60

local 200 120 0 60

safe node

150 330

180

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Extended Disconnection, case 2

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200 60

local 200 0 60

safe node

330

300

40

8 am

30

0

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Global Time

No time synchronization code on the motes

Only need relative time to place all subject interactions in context

Tactic: choose a popular mote!

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Global Time, p2

Everyone likes food…

93.6% of participants received a beacon from one mote in the dining area

it became the global reference clock

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Global Time

mote 101 example

source bcn_seqno local_seqno

450 10 0 ….. ….. 10055 1750 50

global

1700

1750

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Global Time, result

A lookup table

Node ID Global time at start 101 1700 102 1800

… … 145 ???

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You did all this work, so what?

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Takeaways

Sensor networks provide an opportunity improve the understanding of disease spread.

Large-scale deployments can benefit from standard tools for mass programming.

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Discussion 30


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