Epidemiological delays, such as incubation periods, serial intervals, and hospital lengths of stay, are among key quantities in infectious disease epidemiology that inform public health policy and clinical practice. This information is used to inform mathematical and statistical models, which in turn can inform control strategies. There are three main challenges that make delay distributions difficult to estimate. First, the data are commonly censored (e.g., symptom onset may only be reported by date instead of the exact time of day). Second, delays are often right truncated when being estimated in real time (not all events that have occurred have been observed yet). Third, during a rapidly growing or declining outbreak, overrepresentation or underrepresentation, respectively, of recently infected cases in the data can lead to bias in estimates. Studies that estimate delays rarely address all these factors and sometimes report several estimates using different combinations of adjustments, which can lead to conflicting answers and confusion about which estimates are most accurate. In this work, we formulate a checklist of best practices for estimating and reporting epidemiological delays with a focus on the incubation period and serial interval. We also propose strategies for handling common biases and identify areas where more work is needed. Our recommendations can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.

翻译：流行病学延迟，包括潜伏期、代际间隔及住院时长等，是传染病流行病学中指导公共卫生政策与临床实践的关键参数。此类信息用于构建数学模型与统计学模型，进而为防控策略提供依据。延迟分布估计面临三大核心挑战：首先，数据通常存在删失（例如，症状出现时间可能仅按日期而非具体时刻报告）；其次，实时估计时延迟常存在右截断（已发生但未被观测的事件尚未完全记录）；第三，在疫情快速上升或下降阶段，数据中近期感染病例的过度或不足代表性会分别导致估计偏差。现有延迟估计研究鲜少全面考虑上述因素，部分研究通过不同调整组合产生多个估计值，导致结论矛盾且难以判断最优结果。本研究针对潜伏期与代际间隔，系统提出延迟分布估计与报告的最佳实践清单，同时设计常见偏倚的处理策略并明确需深入探索的研究方向。本建议可增强估计结果的稳健性与实用性，为后续传输模型等下游应用中的估计值评估提供方法论指导。