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Evaluating the Performance of Syndromic Surveillance System using High-fidelity Outbreak Simulations
Author(s) -
Tao Tao,
Qi Zhao,
Shaofa Nie,
Lars Palm,
Vinod Diwan,
Biao Xu
Publication year - 2014
Publication title -
online journal of public health informatics
Language(s) - English
Resource type - Journals
ISSN - 1947-2579
DOI - 10.5210/ojphi.v6i1.5095
Subject(s) - outbreak , disease surveillance , computer science , fidelity , data mining , real time computing , false alarm , medicine , disease , machine learning , virology , telecommunications , pathology
This study introduced high-fidelity simulations based on real-world outbreaks for evaluating the performance of syndromic surveillance system. Findings showed that ISSC system was capable to detect the 3 disease outbreaks tested at an early stage, but the practical performance was to a great extent affected by the type and magnitude of outbreak event, the selection of syndromic groups for monitoring, the detection algorithm introduced in the system, and the preferred false alarm rate in real-time surveillance.

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