Despite its critical role in containing outbreaks, the efficacy of contact tracing, measured as the sensitivity of case detection, remains an elusive metric. We estimated the sensitivity of contact tracing by applying unilist capture-recapture methods on data from the 2018–2020 outbreak of Ebola virus disease in the Democratic Republic of the Congo. To compute sensitivity, we applied different distributional assumptions to the zero-truncated count data to estimate the number of unobserved case-patients with any contacts and infected contacts. Geometric distributions were the best-fitting models. Our results indicate that contact tracing efforts identified almost all (n = 792 [99%]) of case-patients with any contacts but only half (n = 207, 48%) of case-patients with infected contacts, suggesting that contact tracing efforts performed well at identifying contacts during the listing stage but performed poorly during the contact follow-up stage. We discuss extensions to our work and potential applications for the ongoing coronavirus pandemic.

尽管在控制疫情方面发挥了关键作用，但以病例检测的敏感性来衡量的接触者追踪的有效性仍然是一个难以捉摸的指标。我们通过对 2018-2020 年刚果民主共和国埃博拉病毒病爆发的数据应用单列表捕获-重新捕获方法来估计接触者追踪的敏感性。为了计算敏感性，我们对零截断计数数据应用了不同的分布假设，以估计具有任何接触者和受感染接触者的未观察到的病例数。几何分布是最佳拟合模型。我们的结果表明，接触者追踪工作可以识别出几乎所有 (n = 792 [99%]) 有任何接触者的病例，但只有一半 (n = 207, 48%) 的有感染者接触者，表明接触者追踪工作在列表阶段识别接触者方面表现良好，但在接触者跟进阶段表现不佳。我们讨论了我们工作的扩展以及对正在进行的冠状病毒大流行的潜在应用。