ArticleJournal of the American Medical Informatics Association : JAMIA2013
Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Exploring Climate Links and Clinical Association of the Diarrheal Disease Using Data From an Upsurge in Dhaka, Bangladesh: A Cross-Sectional Study.Health science reports · 2025Article
- Mathematical models and analysis tools for risk assessment of unnatural epidemics: a scoping review.Frontiers in public health · 2024Article
- Bioterrorism and the Role of the Clinical Microbiology Laboratory.Clinical microbiology reviews · 2016Review
- The current state of bioterrorist attack surveillance and preparedness in the US.Risk management and healthcare policy · 2014Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe discuss the use of structural models for the analysis of biosurveillance related data. METHODS AND
resultsUsing 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.
conclusionsStructural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data.
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