Abstract

Background:

The effective reproductive number, $R_e$, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, $R_e$ estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change.

Objectives:

We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate $R_e$ in near real time, independent of clinical data and without the associated biases.

Methods:

We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based $R_e$ from this incidence.

Results:

The method to estimate $R_e$ from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the $R_e$ estimates from case report data as $R_e$ estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer $R_e$.

Discussion:

To our knowledge, this is the first time $R_e$ has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050

中文翻译：

---

基于废水的 SARS-CoV-2 有效繁殖数估算

摘要

背景：

有效繁殖数，${右}_e$，是监测疾病动态、为区域和国家政策提供信息以及评估干预措施有效性的关键指标。它描述了单个感染者在一段时间内引起的平均新感染数量。迄今为止，${右}_e$估计值基于临床数据，例如观察到的病例、住院情况和/或死亡情况。当临床测试或报告策略发生变化时，这些估计会暂时出现偏差。

目标：

我们表明，废水中严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) RNA 的动态可用于估计${右}_e$近乎实时，独立于临床数据，并且没有相关偏差。

方法：

我们收集了瑞士苏黎世和美国加利福尼亚州圣何塞废水中 SARS-CoV-2 RNA 的纵向测量结果。我们将这些数据与脱落时间动态信息（脱落负荷分布）相结合，以估计与每日 COVID-19 感染发病率成比例的时间序列。我们估计基于废水的${右}_e$从这起事件来看。

结果：

估算方法${右}_e$来自废水的数据对来自两个不同国家和两个废水矩阵的数据发挥了强大的作用。由此产生的估计与${右}_e$根据病例报告数据估计${右}_e$基于观察到的病例、住院情况和死亡人数的估计是相互关联的。我们进一步提供了有关采样频率和卸载负载分布对推断能力的影响的详细信息${右}_e$。

讨论：

据我们所知，这是第一次${右}_e$已从废水中估计。该方法提供了一种低成本、快速且独立的方法来在持续的大流行期间为 SARS-CoV-2 监测提供信息，并且适用于未来针对其他病原体的基于废水的流行病学。https://doi.org/10.1289/EHP10050