Data from: Intrinsic and realized generation intervals in infectious-disease transmission

The generation interval is the interval between the time that an individual is infected by an infector and the time this infector was infected. Its distribution underpins estimates of the reproductive number and hence informs public health strategies. Empirical generation-interval distributions are often derived from contact-tracing data. But linking observed generation intervals to the underlying generation interval required for modeling purposes is surprisingly not straightforward, and misspecifications can lead to incorrect estimates of the reproductive number, with the potential to misguide interventions to stop or slow an epidemic. Here, we clarify the theoretical framework for three conceptually different generation-interval distributions: the ``intrinsic'' one typically used in mathematical models and the ``forward'' and ``backward'' ones typically observed from contact tracing data, looking respectively forward or backward in time. We explain how the relationship between these distributions changes as an epidemic progresses and discuss how empirical generation-interval data can be used to correctly inform mathematical models.

传播间隔（generation interval）指个体被传染源感染的时刻，与该传染源自身被感染的时刻之间的时间区间。其分布是再生数（reproductive number）估计的核心支撑依据，进而可为公共卫生防控策略制定提供科学参考。经验性传播间隔分布通常源自接触者追踪（contact-tracing）数据。但将观测得到的传播间隔与建模所需的理论传播间隔建立关联，实则出乎意料地复杂；若模型设定出现偏差，将导致再生数估计结果失真，甚至可能误导旨在遏制或减缓疫情的干预举措。本文厘清了三类概念迥异的传播间隔分布的理论框架：数学模型中常用的「本征（intrinsic）」传播间隔，以及分别沿时间向前、向后追溯时，从接触者追踪数据中观测得到的「正向（forward）」与「反向（backward）」传播间隔。我们阐释了随着疫情发展，这三类分布间的关系如何动态演变，并探讨了如何利用经验性传播间隔数据，为数学模型提供准确的参数支撑。