Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Integrating syndromic surveillance data across multiple locations: effects on outbreak detection performance.

Ben Y Reis1, Kenneth D Mandl

  • 1Children's Hospital Boston, MA, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 20, 2004
PubMed
Summary

Integrating local and aggregate data in syndromic surveillance systems improves bioterrorism outbreak detection. A hybrid approach combining individual and aggregated models offers the best performance for diverse outbreak scenarios.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Design principles for integrated AI alignment.

Patterns (New York, N.Y.)·2026
Same author

Development and validation of a near-comprehensive RxNorm valueset of opioid medications.

JAMIA open·2026
Same author

What do LLMs value? An evaluation framework for revealing subjective trade-offs in assessment of glycemic control.

Proceedings of machine learning research·2026
Same author

Trends in Suicide Mortality by Method among US Individuals aged 10-24 Years from 1999 to 2024.

medRxiv : the preprint server for health sciences·2026
Same author

Embeddings of clinical codes enable knowledge-grounded AI in medicine.

NPJ digital medicine·2026
Same author

Learning Normal Representations for Blood Biomarkers.

ArXiv·2026

Area of Science:

  • Public Health
  • Epidemiology
  • Biosecurity

Background:

  • Syndromic surveillance systems are crucial for detecting bioterrorist attacks.
  • Regional systems integrate local data from multiple facilities for enhanced monitoring.
  • Evaluating data integration methods is key to improving outbreak detection performance.

Purpose of the Study:

  • To assess how different data integration methods impact outbreak detection in syndromic surveillance.
  • To compare the performance of aggregate versus local models for detecting simulated outbreaks.

Main Methods:

  • Utilized a simulation with a semi-synthetic dataset based on historical hospital visit data.
  • Introduced simulated outbreaks of varying sizes into two hospital datasets.
  • Compared an aggregate model (even distribution) with a local model (single facility distribution).

Related Experiment Videos

Main Results:

  • The aggregate model showed higher sensitivity for evenly distributed outbreaks.
  • Local models demonstrated superior performance for outbreaks localized to a single facility.
  • Outbreak detection performance varied significantly based on the integration method and outbreak distribution.

Conclusions:

  • Both aggregate and local data integration methods offer complementary benefits for outbreak detection.
  • A hybrid syndromic surveillance system, incorporating both individual and aggregate models, is recommended.
  • Further research into multi-level signal integration hierarchies can optimize detection capabilities.