For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and querying symptom status at the time of survey response. For example, in a SARS-CoV-2 testing program at the University of Washington, participants were surveyed at least 28 days after testing positive and asked to report current symptom status. This study design yielded current status data: Outcome measurements for each respondent consisted only of the time of survey response and a binary indicator of whether symptoms had resolved by that time. Such study design benefits from limited risk of recall bias, but analyzing the resulting data necessitates specialized statistical tools. Here, we review methods for current status data and describe a novel application of modern nonparametric techniques to this setting. The proposed approach is valid under weaker assumptions compared to existing methods, allows use of flexible machine learning tools, and handles potential survey nonresponse. From the university study, we estimate that 19% of participants experienced ongoing symptoms 30 days after testing positive, decreasing to 7% at 90 days. Female sex, history of seasonal allergies, fatigue during acute infection, and higher viral load were associated with slower symptom resolution.

翻译：对于传染病而言，表征症状持续时间具有临床和公共卫生重要性。症状持续时间可通过调查感染者并询问其在应答调查时的症状状态进行评估。例如，在华盛顿大学的一项SARS-CoV-2检测项目中，参与者在检测阳性后至少28天接受调查，并被要求报告当前症状状态。该研究设计产生了现时状态数据：每位受访者的结局测量仅包含调查应答时间及截至该时间症状是否已缓解的二元指标。此类研究设计因回忆偏倚风险有限而获益，但分析所得数据需要专门的统计工具。本文综述了现时状态数据的分析方法，并描述了现代非参数技术在此场景下的创新应用。与现有方法相比，所提方法在更弱的假设条件下有效，允许使用灵活的机器学习工具，并能处理潜在的调查无应答问题。根据该大学研究，我们估计19%的参与者在检测阳性30天后仍存在持续症状，至90天时该比例降至7%。女性性别、季节性过敏史、急性感染期疲劳症状及较高病毒载量与症状缓解速度较慢相关。