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基于校园节点的废水监测可实现 COVID-19 病例定位,并证实与周边社区相比,SARS-CoV-2 负担较低。

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.

机构信息

Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada.

Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada.

出版信息

Water Res. 2023 Oct 1;244:120469. doi: 10.1016/j.watres.2023.120469. Epub 2023 Aug 8.

Abstract

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.

摘要

污水监测(WBS)已被确立为一种强大的工具,可以在各级政府指导卫生政策。然而,这种方法在更细粒度的规模上尚未得到很好的评估,包括大的工作场所,如大学校园。2021 年 8 月至 2022 年 4 月,我们使用来自多个互补污水集水区和住宅建筑的 qPCR 检测方法,探索了 SARS-CoV-2 RNA 在卡尔加里大学校园范围内的污水中的出现情况,以及这与为校园服务的市政污水处理厂的水平相比如何。实时接触追踪数据用于评估污水 SARS-CoV-2 负荷与临床确诊病例之间的关联,并评估污水监测作为一种在工作场所进行疾病监测的工具的潜力。污水 SARS-CoV-2 N1 和 N2 RNA 的浓度在六个采样点之间差异很大 - 无论采用几种归一化策略 - 某些集水区的浓度始终比其他集水区高 1-2 个数量级。与特定污水流域中确定的临床病例相比,WBS 提供了一周的领先指标。此外,我们的综合监测策略使我们能够估计校园内每个人的 SARS-CoV-2 总负担,这明显低于周围社区(p≤0.001)。等位基因特异性 qPCR 检测证实,校园内的变异株与整个社区的变异株具有代表性,并且在任何时候,新出现的变异株都不会首先在校园内首次出现。这项研究表明,污水监测如何能够有效地应用于在非常细粒度的规模上定位疾病活动的热点,并预测大型复杂工作场所的疾病负担。

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