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Estimating true prevalence through questionnaire data.

Adam Mielke1, Matt Denwood2, Lasse Engbo Christiansen3

  • 1Department of Applied Mathematics and Computer Science, Dynamical Systems, Technical University of Denmark, Lyngby, Denmark.

Journal of Medical Virology
|July 3, 2023
PubMed
Summary

This study introduces a new analytical method to accurately estimate disease prevalence using voluntary testing data. Incorporating questionnaire data on testing motivation provides unbiased prevalence estimates, crucial for outbreak management.

Keywords:
SARS coronavirusbiostatistics & bioinformaticscoronavirusdata processingepidemiologypandemicstime series analysisvirus classification

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Estimating disease prevalence in populations often relies on testing data, but voluntary participation can introduce bias.
  • Understanding the reasons individuals participate in testing is crucial for accurate epidemiological assessments.

Purpose of the Study:

  • To develop a general analytical method for obtaining unbiased prevalence estimates from voluntary testing programs.
  • To demonstrate the utility of incorporating individual-level questionnaire data on testing motivation.

Main Methods:

  • The method rewrites conditional probabilities of testing, infection, and symptoms.
  • A series of equations are defined to relate estimable quantities (test and questionnaire data) to unbiased prevalence.

Main Results:

  • The proposed analytical method yields unbiased prevalence estimates.
  • Estimated temporal dynamics and agreement with independent estimates suggest robustness.

Conclusions:

  • Integrating questionnaire data on testing motivation significantly enhances the accuracy of prevalence estimation in outbreak settings.
  • This approach offers a valuable tool for public health surveillance and epidemiological research.