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Site Surveillance Using Differential Detection

Site Surveillance Using Differential Detection. Murray Campbell. Acknowledgements. Based on work of Vijay Iyengar, Ed Pednault

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Site Surveillance Using Differential Detection

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  1. Site Surveillance Using Differential Detection Murray Campbell

  2. 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). Site Surveillance Using Differential Detection

  3. Site-Based Bio-Surveillance The monitoring of a geographically constrained site with a relatively stable population for signs of disease outbreak. • 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

  4. 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

  5. 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 Site Surveillance Using Differential Detection

  6. Differential Detection Approach Reference Site Reference Site Target Site Reference Site Reference Site Reference Site Site Surveillance Using Differential Detection

  7. 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 Site Surveillance Using Differential Detection

  8. 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 Site Surveillance Using Differential Detection

  9. Medically-Related Calls Site Surveillance Using Differential Detection

  10. All Calls Site Surveillance Using Differential Detection

  11. Issues • Requires • Good tracking • Significant volumes • Can use • Raw counts • Counts adjusted by domain knowledge • If sites respond differently to some phenomenon Site Surveillance Using Differential Detection

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