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Automated real time constant-specificity surveillance for disease outbreaks
Shannon C Wieland1, John S Brownstein, Bonnie Berger
1Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA. shann@mit.edu <shann@mit.edu>
BMC Medical Informatics and Decision Making
|June 15, 2007
Summary
New outbreak detection models improve disease surveillance by providing constant specificity, leading to better alarm interpretation and cost-effectiveness. This advance enhances real-time public health monitoring.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health Surveillance
Background:
- Disease surveillance relies on detecting abnormal patterns against historical data models.
- The effectiveness of outbreak detection hinges on specificity, as false alarms complicate alarm interpretation.
Purpose of the Study:
- To evaluate the specificity of traditional outbreak detection models.
- To develop a novel method for achieving constant specificity in real-time disease surveillance.
Main Methods:
- Evaluated five traditional models (autoregressive, Serfling, trimmed seasonal, wavelet-based, generalized linear) using 12 years of pediatric respiratory infection data.
- Developed an expectation-variance model using generalized additive modeling to account for visit number variance.
Main Results:
- Traditional models exhibited non-constant specificity, varying by day, month, and year.
- The expectation-variance model achieved constant specificity across all time scales.
- The new model demonstrated earlier detection and improved sensitivity compared to traditional methods.
Conclusions:
- Modeling visit pattern variance allows for real-time detection with consistent, known specificity.
- Constant specificity empowers public health practitioners with improved alarm interpretation and surveillance cost-effectiveness analysis.
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