Practical considerations for measuring the effective reproductive number, Rt.
Practical considerations for measuring the effective reproductive number, Rt.
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DOI：
10.1101/2020.06.18.20134858
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发表时间：
2020-12-01
影响因子：
4.3
通讯作者：
Cobey, Sarah
中科院分区：
文献类型：
--
作者：
Gostic, Katelyn M;McGough, Lauren;Cobey, Sarah
估计的有效繁殖数，R t，是重要的检测疾病传播随时间的变化。在COVID-19大流行期间，政策制定者和公共卫生官员正在使用Rt来评估干预措施的有效性并为政策提供信息。然而，从现有的数据估计RT提出了几个挑战，与流行病的过程的解释的关键影响。本文件的目的是总结这些挑战，用合成数据中的例子加以说明，并在可能的情况下提出建议。对于R t的近实时估计，我们推荐科里等人（2013）的方法，该方法使用时间t之前的数据和感染之间时间分布的经验估计。需要时间t后数据的方法，如Wallinga和Teunis（2004），在概念和方法上不太适合近实时估计，但可能适合于回顾性分析不同时间点感染的个体如何促进传播。我们建议不要使用Bettencourt和Ribeiro（2008）的方法，因为如果不满足基本的结构假设，则所得的Rt估计值可能会有偏差。所有方法共有的两个关键挑战是准确指定世代间隔和从传播后很长时间发生的观察结果重建新感染的时间序列。处理观测延迟的简单方法，例如从分布中减去采样延迟，可能会引入偏差。我们提供了建议，如何减轻这一点和其他技术挑战，并强调开放的问题，在R t estimation.Estimation的有效繁殖数Rt是重要的检测疾病传播的变化随着时间的推移。在2019冠状病毒病（COVID-19）大流行期间，政策制定者和公共卫生官员正在使用RT来评估干预措施的有效性并为政策提供信息。然而，从现有数据估计Rt提出了几个挑战，对解释大流行的过程具有重要意义。本文件的目的是总结这些挑战，用合成数据中的例子加以说明，并在可能的情况下提出建议。对于近实时估计Rt，我们推荐科里及其同事的方法，该方法使用时间t之前的数据和感染之间时间分布的经验估计。需要时间t后数据的方法，如Wallinga和Teunis，在概念和方法上不太适合近实时估计，但可能适合于回顾性分析不同时间点感染的个体如何促成传播。我们建议在使用Bettencourt和Ribeiro方法时要谨慎，因为如果不满足基本的结构假设，则所得的Rt估计值可能会有偏差。所有方法共有的两个关键挑战是准确指定世代间隔和从传播后很长时间发生的观察结果重建新感染的时间序列。处理观测延迟的简单方法，例如从分布中减去采样延迟，可能会引入偏差。我们提供建议，如何减轻这一点和其他技术挑战，并强调开放的问题，在Rt估计。
Estimation of the effective reproductive number, R t , is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using R t to assess the effectiveness of interventions and to inform policy. However, estimation of R t from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of R t , we recommend the approach of Cori et al. (2013), which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis (2004), are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to spread. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting R t estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in R t estimation.Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.