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Related Concept Videos

Principles of Disease Surveillance01:26

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Efficient algorithms for real-time syndromic surveillance.

David Evans1, Ross Sparks2

  • 1Commonwealth Scientific and Industrial Research Organisation, Level 7, STARS Building, 296 Herston Road, Herston, QLD 4029, Australia.

Journal of Biomedical Informatics
|October 25, 2022
PubMed
Summary
This summary is machine-generated.

New surveillance algorithms efficiently detect influenza-like illness outbreaks. By monitoring emergency department (ED) visit times, these tools enable earlier detection of disease spikes, improving hospital resource management.

Keywords:
Emergency department presentations dataNon-homogeneous Weibull processesOutbreak detectionStatistical process controlSyndromic surveillanceTime between events

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Area of Science:

  • Public Health
  • Epidemiology
  • Health Informatics

Background:

  • Influenza-like illness (ILI) outbreaks significantly increase hospital bed demand.
  • Early detection of ILI outbreaks is crucial for effective hospital resource management.
  • Existing surveillance methods may not be sufficiently responsive to rapid changes in disease incidence.

Purpose of the Study:

  • To evaluate the efficiency of novel surveillance algorithms in detecting ILI outbreaks.
  • To determine if algorithms responsive to a wide range of anomalous decreases in presentation times offer early outbreak detection.
  • To compare the performance of multi-EWMA algorithms against single-EWMA algorithms for ILI surveillance.

Main Methods:

  • Utilized emergency department (ED) presentation data from Queensland public hospitals (2017-2020).
  • Developed surveillance algorithms flagging potential outbreaks based on anomalously short times between successive ILI ED presentations.
  • Employed algorithms monitoring three exponentially weighted moving averages (EWMAs) and single EWMA algorithms, using dynamic control limits.

Main Results:

  • The algorithm monitoring three EWMAs demonstrated significantly earlier detection of outbreaks compared to single EWMA algorithms.
  • Performance was assessed using the 2019 seasonal influenza and early 2020 COVID-19 outbreaks.
  • The multi-EWMA approach proved more effective in identifying the onset of epidemic events.

Conclusions:

  • Surveillance algorithms sensitive to broad decreases in ED presentation intervals are highly efficient for hospital-level ILI outbreak detection.
  • This approach supports timely resource allocation during public health emergencies.
  • The findings highlight the potential of advanced time-series analysis in infectious disease surveillance.