measuring a human contact network for epidemiology research
TRANSCRIPT
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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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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