Back to Search
Start Over
Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels
- Source :
- Journal of the American Medical Informatics Association : JAMIA. 20(3)
- Publication Year :
- 2012
-
Abstract
- 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.
- Subjects :
- Receiver operating characteristic
Computer science
Outbreak
Health Informatics
Kalman filter
computer.software_genre
Missing data
Research and Applications
Bioterrorism
Models, Biological
Disease Outbreaks
Data set
Anthrax
Biosurveillance
ROC Curve
Humans
Anomaly detection
Isolation (database systems)
Data mining
computer
Algorithms
Subjects
Details
- ISSN :
- 1527974X
- Volume :
- 20
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Journal of the American Medical Informatics Association : JAMIA
- Accession number :
- edsair.doi.dedup.....42b1e9b50a9acfef8910ad33ecf8add9