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A Bayesian system to detect and characterize overlapping outbreaks.

John M Aronis1, Nicholas E Millett1, Michael M Wagner2

  • 1Real-time Outbreak and Disease Surveillance Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.

Journal of Biomedical Informatics
|August 12, 2017
PubMed
Summary
This summary is machine-generated.

This study presents a Bayesian system to detect and model overlapping influenza outbreaks using emergency department data. The system accurately identifies and characterizes multiple concurrent influenza epidemics.

Keywords:
Bayesian modelingInfluenzaOutbreak characterizationOutbreak detection

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Area of Science:

  • Epidemiology
  • Infectious Disease Modeling
  • Public Health Surveillance

Background:

  • Influenza outbreaks pose a significant public health threat due to diverse strains and varied demographic impacts.
  • Recognizing and characterizing multiple, concurrent influenza outbreaks is crucial for effective public health response.

Purpose of the Study:

  • To develop and evaluate a Bayesian system for identifying and modeling overlapping influenza outbreaks.
  • To leverage emergency department patient care reports for real-time epidemiological analysis.

Main Methods:

  • Utilized natural language processing (NLP) to extract clinical findings from patient care reports.
  • Implemented a case detection system to generate disease likelihoods.
  • Employed a multiple outbreak detection system to model overlapping epidemics.

Main Results:

  • The Bayesian system successfully recognized and characterized simulated and real-world overlapping influenza outbreaks.
  • Demonstrated the feasibility of using emergency department data for advanced outbreak surveillance.

Conclusions:

  • The developed Bayesian approach offers a promising method for monitoring complex influenza epidemics.
  • Further extensions of the system show potential for enhanced infectious disease surveillance.