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Summary
This summary is machine-generated.

Infectious disease surveillance relies on case counts and test positivity, but these metrics are influenced by testing levels. A new framework integrates testing volume, positive cases, and positivity rates for better interpretation.

Keywords:
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Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Biostatistics

Background:

  • Laboratory-confirmed case counts and test positivity are standard metrics for infectious disease surveillance.
  • These metrics are significantly influenced by testing intensity, complicating accurate interpretation of disease activity.
  • Ascertainment bias, stemming from variations in testing, can distort surveillance data.

Purpose of the Study:

  • To introduce a systematic and generalizable framework for infectious disease surveillance.
  • To address the challenges posed by testing intensity and ascertainment bias in interpreting surveillance data.
  • To demonstrate the value of integrating multiple indicators for improved situational assessment and communication.

Main Methods:

  • Development of a framework considering the number of tests performed, number of test-positive counts, and test positivity.
  • Explicit incorporation of ascertainment bias into the analytical approach.
  • Systematic evaluation of the interplay between these indicators.

Main Results:

  • The proposed framework provides a more robust method for monitoring disease activity.
  • Integrating testing volume, positive counts, and positivity rates enhances data interpretation.
  • The framework improves situational assessment and communication during public health surveillance.

Conclusions:

  • A comprehensive framework integrating testing volume, positive case counts, and test positivity is crucial for accurate infectious disease surveillance.
  • Accounting for ascertainment bias is essential for reliable interpretation of surveillance data.
  • This approach enhances the utility of surveillance data for public health decision-making.