Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels

We discuss the use of structural models for the analysis of biosurveillance related data. Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discu...

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Bibliographic Details
Published in:Journal of the American Medical Informatics Association : JAMIA 2013-05, Vol.20 (3), p.435-440
Main Authors: Cheng, Karen Elizabeth, Crary, David J, Ray, Jaideep, Safta, Cosmin
Format: Article
Language:English
Subjects:
Algorithms
Anthrax
Biosurveillance - methods
Bioterrorism
Disease Outbreaks
Humans
Models, Biological
Research and Applications
ROC Curve
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Summary:We discuss the use of structural models for the analysis of biosurveillance related data. Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm. Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data.
ISSN:1067-5027
1527-974X
DOI:10.1136/amiajnl-2012-000945