Improving Outbreak Detection with Recursive Least Squares
Author Information
Author(s): Najmi Amir-Homayoon, Howard Burkom
Primary Institution: The Johns Hopkins University Applied Physics Laboratory
Hypothesis
Can a recursive least squares method improve the detection of outbreaks in syndromic surveillance data?
Conclusion
The modified RLS method provides better sensitivity for detecting anomalies in biosurveillance data compared to the baseline W2 method.
Supporting Evidence
- The modified RLS method showed improved sensitivity at multiple background alert rates.
- Detection performance was compared to the CDC's W2 method using 16 sets of syndromic data.
- The study found that the RLS method consistently outperformed the W2 method, especially for weak signals.
Takeaway
This study shows a new way to predict health data trends that helps find outbreaks faster, using a special math method.
Methodology
The study used an adaptive recursive least squares method to analyze syndromic data and correct for weekly patterns.
Limitations
The method requires a sufficient scale of time series data and a significant amount of historical data for effective predictions.
Participant Demographics
Data from military outpatient clinics and civilian physician office visits in large metropolitan areas.
Digital Object Identifier (DOI)
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