Utilizing a Capture-Recapture Strategy to Accelerate Infectious Disease Surveillance

TL;DR

This paper introduces a novel capture-recapture method that incorporates diagnostic test inaccuracies and an anchor stream design to improve infectious disease surveillance efficiency and accuracy.

Contribution

It develops an improved CRC analysis strategy with Bayesian credible intervals that accounts for test misclassification and enhances disease prevalence estimation.

Findings

Method outperforms traditional CRC in simulations

Provides more accurate prevalence estimates with fewer resources

Demonstrates practical utility for disease monitoring

Abstract

Monitoring key elements of disease dynamics (e.g., prevalence, case counts) is of great importance in infectious disease prevention and control, as emphasized during the COVID-19 pandemic. To facilitate this effort, we propose a new capture-recapture (CRC) analysis strategy that takes misclassification into account from easily-administered, imperfect diagnostic test kits, such as the Rapid Antigen Test-kits or saliva tests. Our method is based on a recently proposed "anchor stream" design, whereby an existing voluntary surveillance data stream is augmented by a smaller and judiciously drawn random sample. It incorporates manufacturer-specified sensitivity and specificity parameters to account for imperfect diagnostic results in one or both data streams. For inference to accompany case count estimation, we improve upon traditional Wald-type confidence intervals by developing an adapted Bayesian credible interval for the CRC estimator that yields favorable frequentist coverage properties. When feasible, the proposed design and analytic strategy provides a more efficient solution than traditional CRC methods or random sampling-based biased-corrected estimation to monitor disease prevalence while accounting for misclassification. We demonstrate the benefits of this approach through simulation studies that underscore its potential utility in practice for economical disease monitoring among a registered closed population.