Document Detail

Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels.
MedLine Citation:
PMID:  23037798     Owner:  NLM     Status:  MEDLINE    
OBJECTIVE: We discuss the use of structural models for the analysis of biosurveillance related data.
METHODS AND RESULTS: 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.
CONCLUSIONS: 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.
Karen Elizabeth Cheng; David J Crary; Jaideep Ray; Cosmin Safta
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-10-04
Journal Detail:
Title:  Journal of the American Medical Informatics Association : JAMIA     Volume:  20     ISSN:  1527-974X     ISO Abbreviation:  J Am Med Inform Assoc     Publication Date:  2013 May 
Date Detail:
Created Date:  2013-04-12     Completed Date:  2013-07-10     Revised Date:  2014-05-08    
Medline Journal Info:
Nlm Unique ID:  9430800     Medline TA:  J Am Med Inform Assoc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  435-40     Citation Subset:  IM    
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MeSH Terms
Biosurveillance / methods*
Disease Outbreaks*
Models, Biological*
ROC Curve

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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