IBM T. J. Watson Research Center
Site Surveillance Using Differential Detection
Murray Campbell
© 2004 IBM Corporation

IBM T. J. Watson Research Center
Acknowledgements
 Based on work of Vijay Iyengar, Ed Pednault
 This material is based upon work supported by the Air Force Research Laboratory(AFRL)/Defense
Advanced Research Projects Agency (DARPA) under AFRL Contract No. F30602-01-C-0184. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the AFRL and/or DARPA. Approved for Public
Release, Distribution Unlimited (5/3/2004).
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Site-Based Bio-Surveillance
The monitoring of a geographically constrained site with a relatively stable population for signs of disease outbreak.
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 Example of sites could include work sites, university campuses, or military bases
 The population need not be present 24 hours a day
Site Surveillance Using Differential Detection 2004-02-19 © 2004 IBM Corporation

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What makes “Site-Based” Bio-Surveillance Different?
 Increased data availability
– Central authority for permissions
– Centralized data collection
 “Permissive”
– “Sensitive” data more likely to be available
 Relatively stable population
– May be more homogenous than general population
 Geographically constrained
– Spatial considerations are greatly reduced or eliminated
Site Surveillance Using Differential Detection 2004-02-19 © 2004 IBM Corporation

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Differential Detection Approach
 Define sites (regions) that normally track each other
– Determine appropriate model for measured quantities
• Quantify normal variation in the tracking
 Detect significant deviations in the tracking
– Signifies event affecting one of the sites
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Differential Detection Approach
Reference Site
Reference Site
Target Site
Reference Site
Reference Site
Reference Site
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Experiments
 Monitor phone calling patterns at two IBM sites
– Yorktown, Hawthorne (10 miles apart)
–
Counts of calls/callers to medical facilities
– Counts of all calls/callers
–
Currently being collected on a daily basis
– Privacy ensured through
• Anonymization of calling number
• No reporting of called number
© 2004 IBM Corporation 7 Site Surveillance Using Differential Detection 2004-02-19

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Method
 Assume underlying Poisson process
 Define two time windows
– History, Test
 Model ratio of counts in a window
 Use Chi-squared statistic to detect deviations
– Empirical variance estimate

2   i

T
( t i
 



 r i
)
2
© 2004 IBM Corporation Site Surveillance Using Differential Detection 2004-02-19

IBM T. J. Watson Research Center
Medically-Related Calls
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All Calls
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Issues
 Requires
– Good tracking
–
Significant volumes
 Can use
– Raw counts
– Counts adjusted by domain knowledge
• If sites respond differently to some phenomenon
11 Site Surveillance Using Differential Detection 2004-02-19 © 2004 IBM Corporation