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.

翻译：监测疾病动态的关键要素（如患病率、病例数）在传染病预防控制中至关重要，这一点在COVID-19大流行期间尤为凸显。为支持这项工作，我们提出了一种新的捕获-再捕获分析策略，该策略能够处理来自易于操作但存在缺陷的诊断检测试剂盒（例如快速抗原检测试剂盒或唾液检测）中的误分类问题。我们的方法基于近期提出的"锚定流"设计，即在现有自愿监测数据流的基础上补充一个规模较小且经过审慎抽取的随机样本。该方法将制造商指定的灵敏度和特异度参数纳入考量，以在一个或两个数据流中校正不完美的诊断结果。为配合病例数估计的统计推断，我们改进了传统的Wald型置信区间，针对捕获-再捕获估计量开发了一种适应性贝叶斯可信区间，该区间具有良好的频率学派覆盖性质。在可行的情况下，所提出的设计与分析策略相比传统捕获-再捕获方法或基于随机样本的偏倚校正估计，能够更高效地监测疾病患病率，同时处理误分类问题。我们通过模拟研究验证了该方法在注册封闭人群中实现经济型疾病监测的实际应用潜力。