Generalized AMOC curves for evaluation and improvement of event surveillance.

Xia Jiang, Gregory F. Cooper, Daniel Neill

Research output: Contribution to journalArticle

Abstract

We introduce Generalized Activity Monitoring Operating Characteristic (G-AMOC) curves, a new framework for evaluation of outbreak detection systems. G-AMOC curves provide a new approach to evaluating and improving the timeliness of disease outbreak detection by taking the user's response protocol into account and considering when the user will initiate an investigation in response to the system's alerts. The standard AMOC curve is a special case of G-AMOC curves that assumes a trivial response protocol (initiating a new and separate investigation in response to each alert signal). Practical application of a surveillance system is often improved, however, by using more elaborate response protocols, such as grouping alerts or ignoring isolated signals. We present results of experiments demonstrating that we can use G-AMOC curves as 1) a descriptive tool, to provide a more accurate comparison of systems than the standard AMOC curve, and 2) as a prescriptive tool, to choose appropriate response protocols for a detection system, and thus improve its performance.

Original languageEnglish (US)
Pages (from-to)281-285
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2009
StatePublished - Dec 1 2009

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Disease Outbreaks

ASJC Scopus subject areas

  • Medicine(all)

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Generalized AMOC curves for evaluation and improvement of event surveillance. / Jiang, Xia; Cooper, Gregory F.; Neill, Daniel.

In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, Vol. 2009, 01.12.2009, p. 281-285.

Research output: Contribution to journalArticle

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