Bio-ALIRT: Biosurveillance Detection Algorithm Evaluation on Historic Outbreak Data

Historic de-identified medical data from five metropolitan areas over 23 months was used to evaluate outbreak detection algorithms. The dataset includes International Classification of Diseases, Ninth Revision (ICD-9) codes related to respiratory and gastrointestinal illness syndromes. David W. Siegrist of the Potomac Institute for Policy Studies led this effort to assess algorithm sensitivity and timeliness.

Use Cases

• Benchmarking outbreak detection algorithm performance based on labeled natural disease outbreaks.
• Analyzing time-series patterns of respiratory and gastrointestinal syndrome counts based on ICD-9 codes.
• Evaluating the trade-off between detection sensitivity and false-alert rates in a public health context.

Strengths

• Data spans 23 months from five metropolitan areas, providing a multi-city temporal perspective.
• Includes labeled natural disease outbreaks for algorithm training and testing.
• Focuses on two specific illness syndromes (respiratory and gastrointestinal) relevant to biosurveillance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Potomac Institute for Policy Studies

Collection Method

Historic de-identified medical data was obtained, and an outbreak detection group identified and labeled natural disease outbreaks within it.

Time Range

23 months (specific dates unknown)

Geography

Five metropolitan areas (locations unspecified)