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高分辨率管内 SARS-CoV-2 监测有助于进行知情干预。

High-resolution within-sewer SARS-CoV-2 surveillance facilitates informed intervention.

机构信息

University of Colorado Boulder, Department of Civil, Environmental, and Architectural Engineering, 1111 Engineering Drive, Boulder, CO 80309, United States; University of Colorado Boulder, Environmental Engineering Program, 4001 Discovery Dr, Boulder, CO 80303, United States.

University of Colorado Boulder, BioFrontiers Institute, 3415 Colorado Avenue, Boulder, CO 80303, United States.

出版信息

Water Res. 2021 Oct 1;204:117613. doi: 10.1016/j.watres.2021.117613. Epub 2021 Aug 28.

Abstract

To assist in the COVID-19 public health guidance on a college campus, daily composite wastewater samples were withdrawn at 20 manhole locations across the University of Colorado Boulder campus. Low-cost autosamplers were fabricated in-house to enable an economical approach to this distributed study. These sample stations operated from August 25th until November 23rd during the fall 2020 semester, with 1512 samples collected. The concentration of SARS-CoV-2 in each sample was quantified through two comparative reverse transcription quantitative polymerase chain reactions (RT-qPCRs). These methods were distinct in the utilization of technical replicates and normalization to an endogenous control. (1) Higher temporal resolution compensates for supply chain or other constraints that prevent technical or biological replicates. (2) The data normalized by an endogenous control agreed with the raw concentration data, minimizing the utility of normalization. The raw wastewater concentration values reflected SARS-CoV-2 prevalence on campus as detected by clinical services. Overall, combining the low-cost composite sampler with a method that quantifies the SARS-CoV-2 signal within six hours enabled actionable and time-responsive data delivered to key stakeholders. With daily reporting of the findings, wastewater surveillance assisted in decision making during critical phases of the pandemic on campus, from detecting individual cases within populations ranging from 109 to 2048 individuals to monitoring the success of on-campus interventions.

摘要

为协助科罗拉多大学博尔德分校的校园 COVID-19 公共卫生指导工作,我们从校园的 20 个检查井位置抽取了每日综合污水样本。我们在内部制造了低成本自动取样器,以实现这种分布式研究的经济方法。这些采样站从 2020 年秋季 8 月 25 日运营到 11 月 23 日,共采集了 1512 个样本。通过两种比较逆转录定量聚合酶链反应(RT-qPCR)来定量每个样本中 SARS-CoV-2 的浓度。这些方法在使用技术重复和内源性对照物进行归一化方面有所不同。(1)更高的时间分辨率弥补了供应链或其他限制因素,这些限制因素会阻止技术或生物学重复。(2)通过内源性对照物归一化的数据与原始浓度数据一致,最大限度地减少了归一化的实用性。原始废水浓度值反映了临床服务在校园内检测到的 SARS-CoV-2 流行情况。总的来说,将低成本综合采样器与在六小时内定量 SARS-CoV-2 信号的方法相结合,为关键利益相关者提供了可操作且及时响应的数据。通过每日报告研究结果,废水监测在校园内大流行的关键阶段协助了决策制定,从在 109 到 2048 个人的人群中检测到个体病例,到监测校园干预措施的成功。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a88/8402945/71b5429c0e5f/ga1_lrg.jpg

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