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Modeling COVID-19 contact-tracing using the ratio regression capture-recapture approach.

Dankmar Böhning1, Rattana Lerdsuwansri2, Patarawan Sangnawakij2

  • 1Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK.

Biometrics
|February 16, 2023
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Summary

Contact tracing is vital for infectious disease control. A new capture-recapture method using ratio regression estimated 83% completeness in Thailand

Keywords:
Covid-19 transmission in Thailandcontact tracingcount distribution modelingratio regressionzero-truncation

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Contact tracing is a cornerstone of infectious disease outbreak control.
  • Accurate estimation of case detection completeness is crucial for effective public health interventions.
  • Traditional methods may not fully capture the complexities of real-world contact tracing data.

Purpose of the Study:

  • To introduce and apply a novel capture-recapture approach utilizing ratio regression for estimating contact tracing completeness.
  • To assess the effectiveness of this methodology using COVID-19 contact tracing data from Thailand.

Main Methods:

  • A capture-recapture framework was employed, based on ratio regression, a flexible tool for count data modeling.
  • A simple weighted straight-line approach, encompassing Poisson and geometric distributions, was utilized.
  • The methodology was specifically applied to COVID-19 contact tracing data from Thailand.

Main Results:

  • The ratio regression-based capture-recapture method successfully estimated the completeness of case detection.
  • Analysis of COVID-19 contact tracing data from Thailand yielded an estimated completeness of 83%.
  • A 95% confidence interval for completeness was determined to be between 74% and 93%.

Conclusions:

  • The developed ratio regression capture-recapture method provides a robust tool for evaluating contact tracing completeness.
  • This approach offers valuable insights for optimizing public health strategies during infectious disease outbreaks.
  • The study demonstrates the practical applicability of advanced statistical modeling in real-world epidemiological surveillance.