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Monitoring key elements of disease dynamics (e.g., prevalence, case counts)is of great importance in infectious disease prevention and control, asemphasized during the COVID-19 pandemic. To facilitate this effort, we proposea new capture-recapture (CRC) analysis strategy that takes misclassificationinto account from easily-administered, imperfect diagnostic test kits, such asthe Rapid Antigen Test-kits or saliva tests. Our method is based on a recentlyproposed "anchor stream" design, whereby an existing voluntary surveillancedata stream is augmented by a smaller and judiciously drawn random sample. Itincorporates manufacturer-specified sensitivity and specificity parameters toaccount for imperfect diagnostic results in one or both data streams. Forinference to accompany case count estimation, we improve upon traditionalWald-type confidence intervals by developing an adapted Bayesian credibleinterval for the CRC estimator that yields favorable frequentist coverageproperties. When feasible, the proposed design and analytic strategy provides amore efficient solution than traditional CRC methods or random sampling-basedbiased-corrected estimation to monitor disease prevalence while accounting formisclassification. We demonstrate the benefits of this approach throughsimulation studies that underscore its potential utility in practice foreconomical disease monitoring among a registered closed population.

Utilizing a Capture-Recapture Strategy to Accelerate Infectious Disease Surveillance