发表机构
Division of Biostatistics, College of Public Health, The Ohio State University; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet（俄亥俄州立大学公共卫生学院生物统计系; 卡罗林斯卡医学院医学流行病学与生物统计系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出反事实框架，通过建模检测过程（含定期、症状及接触者追踪检测）实现无偏估计传染病患病率，无需显式建模传播动态，并以俄亥俄州立大学COVID-19数据为例验证。
AI 中文摘要
本文研究了在包含定期检测、症状检测和接触者追踪检测的纵向检测项目下估计传染病患病率的问题。我们的研究受俄亥俄州立大学数据的启发，该校在2020年秋季学期实施了每周一次的强制性COVID-19检测和隔离项目，并辅以对出现症状的个体和已识别的接触者进行额外检测。在这种设置下，被检测的概率取决于症状或接触者追踪状态，从而形成了一个复杂的观测过程。我们开发了一个反事实框架，将观测过程与一个假设性的、感染被预防的过程联系起来。这一表述使得通过建模检测过程（可能采用非参数方法）来无偏估计疾病患病率成为可能，而无需显式建模传播动态，即使检测和感染过程是联合依赖的。
英文摘要
This paper addresses the problem of estimating infectious disease prevalence under longitudinal testing programs that include scheduled, symptomatic, and contact-tracing testing. Our study is motivated by data from The Ohio State University, where a mandatory once-per-week COVID-19 testing and isolation program was implemented during the Fall 2020 semester, supplemented by additional testing for symptomatic individuals and identified contacts. In this setting, the probability of being tested depends on symptoms or contact-tracing status, creating a complex observation process. We develop a counterfactual framework that links the observation process to a hypothetical process in which infection is prevented. This formulation enables unbiased estimation of disease prevalence by modeling the testing process, possibly nonparametrically, without requiring explicit modeling of transmission dynamics, even though the testing and infection processes are jointly dependent.
CommentsAccepted for publication in The Annals of Applied Statistics
