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

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.