The 130th Annual Meeting of APHA

4079.0: Tuesday, November 12, 2002 - 8:30 AM

Abstract #52868

Essence II alerting algorithm methodology

Howard S. Burkom, PhD, National Security Technology Department, Johns Hopkins Applied Physics Laboratory, 1234, Washington, DC 20000, 240-228-4361,

The U.S. Department of Defense Global Emerging Infections System (DoD-GEIS) has developed the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) to enable outbreak alerting using syndromic surveillance. This system monitors over 100 primary care and emergency clinics in the National Capital Area, and since the September 2001 terrorist attacks, approximately 100,000 claims per day are collected from military treatment facilities worldwide. The ESSENCE II system extends this capability for both military and civilian populations in the National Capital Region. This extension adds more health care data sources along with a variety of nontraditional sources, including daily records of pharmacy sales, school absenteeism, and animal health.

For early alerting capability, analysts from DoD-GEIS and the Johns Hopkins Applied Physics Laboratory have implemented spatial-temporal anomaly detection methods to detect localized outbreaks and purely temporal methods for the low-level, scattered threat. The usefulness of these algorithms requires both high sensitivity and low false alarm rates.

Our spatial-temporal methodology is based on the Kulldorff scan statistic. A key issue is that the input data streams are typically not population-based. We have used both modeling and recent historical data distributions to obtain background spatial distributions. Data analyses have provided guidance on how to condition and model input data to avoid excessive clustering.

Temporal alerting algorithms employ autoregressive modeling for larger, structured data streams and control chart-based testing otherwise. Testing of all algorithms includes series of Monte Carlo runs using randomized, epicurve-like signals.

Learning Objectives:

Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.

Statistical Issues in Biosurveillance

The 130th Annual Meeting of APHA