Practical considerations for measuring the effective reproductive number, Rt.
Practical considerations for measuring the effective reproductive number, Rt.
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DOI：
10.1371/journal.pcbi.1008409
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发表时间：
2020-12
影响因子：
4.3
通讯作者：
Cobey S
中科院分区：
文献类型：
--
作者：
Gostic KM;McGough L;Baskerville EB;Abbott S;Joshi K;Tedijanto C;Kahn R;Niehus R;Hay JA;De Salazar PM;Hellewell J;Meakin S;Munday JD;Bosse NI;Sherrat K;Thompson RN;White LF;Huisman JS;Scire J;Bonhoeffer S;Stadler T;Wallinga J;Funk S;Lipsitch M;Cobey S
有效繁殖数 Rt 的估计对于检测疾病传播随时间的变化非常重要。在 2019 年冠状病毒病 (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，根据现有数据估计 Rt 提出了一些挑战，这对解释大流行的进程具有重要影响。本文件的目的是总结这些挑战，用合成数据的示例来说明它们，并在可能的情况下提出建议。对于 Rt 的近实时估计，我们推荐 Cori 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 之后数据的方法（例如 Wallinga 和 Teunis）在概念和方法上不太适合近实时估计，但可能适合对不同时间点感染的个体如何促进传播进行回顾性分析。我们建议谨慎使用源自 Bettencourt 和 Ribeiro 方法的方法，因为如果不满足基本结构假设，所得的 Rt 估计值可能会出现偏差。所有方法共有的两个关键挑战是准确指定生成间隔以及根据传播时刻后很久发生的观察重建新感染的时间序列。处理观察延迟的简单方法（例如减去从分布中采样的延迟）可能会引入偏差。我们就如何缓解这一挑战和其他技术挑战提供建议，并强调 Rt 估计中的未解决问题。有效再生数 Rt 是一个关键的流行病参数，用于评估流行病是否正在增长、缩小或保持稳定。 Rt 估计值可用作流行病增长的近实时指标或评估干预措施的有效性。但由于感染和病例观察之间存在延迟，近实时估计 Rt 并正确推断 Rt 变化的时间具有挑战性。在这里，我们概述了准确及时的 Rt 估计的挑战和最佳实践。
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. The effective reproductive number Rt is a key epidemic parameter used to assess whether an epidemic is growing, shrinking, or holding steady. Rt estimates can be used as a near real-time indicator of epidemic growth or to assess the effectiveness of interventions. But due to delays between infection and case observation, estimating Rt in near real time, and correctly inferring the timing of changes in Rt, is challenging. Here, we provide an overview of challenges and best practices for accurate and timely Rt estimation.