Institute for Integrative Biology, Department of Environmental System Science, ETH Zürich, Zürich, Switzerland.
J R Soc Interface. 2021 Jan;18(174):20200756. doi: 10.1098/rsif.2020.0756. Epub 2021 Jan 6.
The timing of transmission plays a key role in the dynamics and controllability of an epidemic. However, observing generation times-the time interval between the infection of an infector and an infectee in a transmission pair-requires data on infection times, which are generally unknown. The timing of symptom onset is more easily observed; generation times are therefore often estimated based on serial intervals-the time interval between symptom onset of an infector and an infectee. This estimation follows one of two approaches: (i) approximating the generation time distribution by the serial interval distribution or (ii) deriving the generation time distribution from the serial interval and incubation period-the time interval between infection and symptom onset in a single individual-distributions. These two approaches make different-and not always explicitly stated-assumptions about the relationship between infectiousness and symptoms, resulting in different generation time distributions with the same mean but unequal variances. Here, we clarify the assumptions that each approach makes and show that neither set of assumptions is plausible for most pathogens. However, the variances of the generation time distribution derived under each assumption can reasonably be considered as upper (approximation with serial interval) and lower (derivation from serial interval) bounds. Thus, we suggest a pragmatic solution is to use both approaches and treat these as edge cases in downstream analysis. We discuss the impact of the variance of the generation time distribution on the controllability of an epidemic through strategies based on contact tracing, and we show that underestimating this variance is likely to overestimate controllability.
传播时机在传染病的动力学和可控性中起着关键作用。然而，观察到代时（感染者和感染者在传播对中感染的时间间隔）需要有关感染时间的数据，而这些数据通常是未知的。症状发作的时间更容易观察到；因此，代时通常基于序列间隔（感染者和感染者症状发作之间的时间间隔）来估计。这种估计遵循两种方法之一：（i）通过序列间隔分布来近似代时分布，或（ii）从序列间隔和潜伏期（个体中感染和症状发作之间的时间间隔）分布推导出代时分布。这两种方法对传染性和症状之间的关系做出了不同的假设（并不总是明确说明），导致具有相同平均值但方差不同的不同代时分布。在这里，我们澄清了每种方法所做的假设，并表明这两种假设都不适用于大多数病原体。然而，根据每种假设得出的代时分布的方差可以合理地视为上限（用序列间隔进行近似）和下限（从序列间隔推导）。因此，我们建议采用实用的解决方案是同时使用这两种方法，并将其视为下游分析中的边缘情况。我们通过基于接触追踪的策略讨论了代时分布的方差对传染病可控性的影响，并表明低估该方差很可能高估可控性。
