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Related Experiment Video

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Disentangling reporting and disease transmission.

Eamon B O'Dea1, John M Drake1

  • 1Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, 140 E. Green Street, Athens, GA, 30602, USA.

Theoretical Ecology
|September 23, 2021
PubMed
Summary

This study introduces new indicators for infectious disease surveillance that are unaffected by reporting inconsistencies. These metrics can help detect early signs of an epidemic threshold, distinguishing increased transmission from reporting changes.

Keywords:
Birth-death-immigration processDisease emergenceEarly warningState space modelSurveillance

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Statistical Modeling

Background:

  • Second-order statistics can indicate system stability and predict critical transitions.
  • Early warning indicators are crucial for infectious disease surveillance and epidemic threshold detection.
  • Previous work lacked analytical treatment of imperfect observation effects on these indicators.

Purpose of the Study:

  • To develop and analyze early warning indicators for infectious disease emergence.
  • To analytically treat the impact of imperfect observation (reporting probability) on indicator behavior.
  • To identify indicators insensitive to reporting probability for distinguishing transmission vs. reporting trends.

Main Methods:

  • Calculating expected values for moments of reported cases using binomial/negative binomial distributions.
  • Modeling reported cases based on a birth-death-immigration process with deaths.
  • Employing simulation studies to validate indicator performance and assess variance reduction strategies.

Main Results:

  • The normalized second factorial moment and decay time are identified as reporting probability-insensitive indicators.
  • Simulation demonstrated the ability to differentiate increased transmission from increased reporting using these indicators.
  • High variance in estimates was observed, but ensemble averaging across multiple time series can reduce this variance.

Conclusions:

  • Reporting probability-insensitive indicators offer a robust tool for infectious disease surveillance.
  • These indicators can help distinguish genuine changes in disease transmission from changes in reporting practices.
  • Ensemble averaging is a viable strategy to improve the reliability of these early warning signals.