Monitoring the incidence of new infections during a pandemic is critical for an effective public health response. General population prevalence surveys for SARS-CoV-2 can provide high-quality data to estimate incidence. However, estimation relies on understanding the distribution of the duration that infections remain detectable. This study addresses this need using data from the Coronavirus Infection Survey (CIS), a long-term, longitudinal, general population survey conducted in the UK. Analyzing these data presents unique challenges, such as doubly interval censoring, undetected infections, and false negatives. We propose a Bayesian nonparametric survival analysis approach, estimating a discrete-time distribution of durations and integrating prior information derived from a complementary study. Our methodology is validated through a simulation study, including its resilience to model misspecification, and then applied to the CIS dataset. This results in the first estimate of the full duration distribution in a general population, as well as methodology that could be transferred to new contexts.

翻译：在疫情期间监测新感染发生率对有效的公共卫生应对至关重要。针对SARS-CoV-2的普通人群流行率调查可为发病率估计提供高质量数据，但该估计依赖于对感染可检出持续时间分布的理解。本研究利用英国开展的冠状病毒感染调查（CIS）——一项长期纵向普通人群调查——的数据来应对这一需求。分析这些数据面临双重区间删失、未检出感染和假阴性等独特挑战。我们提出一种贝叶斯非参数生存分析方法，估计持续时间的离散时间分布，并整合来自互补研究的先验信息。通过模拟研究验证了该方法的有效性（包括其对模型误设的稳健性），随后将其应用于CIS数据集。这首次实现了对普通人群完整持续时间分布的估计，并形成了可迁移至新场景的方法体系。