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在一个大城市区域识别用于严重急性呼吸综合征冠状病毒2(SARS-CoV-2)废水监测的上游哨点社区地点。

Identification of sentinel upstream community sites for wastewater surveillance of SARS-CoV-2 in a large urban area.

作者信息

Oswald Claire, Melles Stephanie, Gilbride Kimberley, Goitom Eyerusalem, Ariano Sarah, Johnston Alexandra, Hataley Eden, Tehrani Amir, Dannah Nora, Aqeel Hussain, Wellen Christopher, Li James, Liss Steven

机构信息

Department of Geography and Environmental Studies, Toronto Metropolitan University, Toronto, ON, Canada; Urban Water TMU Research Centre, Toronto Metropolitan University, Toronto, ON, Canada.

Department of Chemistry & Biology, Toronto Metropolitan University, Toronto, ON, Canada; Urban Water TMU Research Centre, Toronto Metropolitan University, Toronto, ON, Canada.

出版信息

Water Res. 2025 Jun 17;284:123958. doi: 10.1016/j.watres.2025.123958.

Abstract

Wastewater-based surveillance (WBS) captures the presence of disease in a community of people regardless of symptom status and supports public health interventions to mitigate the spread of disease. Wastewater-based surveillance can be applied to a variety of spatial scales and population sizes, particularly where households are served by municipal wastewater collection systems (e.g., large areas served by a single wastewater treatment plant (WWTP), smaller areas contained within a single neighbourhood, individual facilities). Since the onset of the COVID-19 pandemic in 2020, governments have had to make critical decisions on where, and at what scale, to implement WBS. Population size, health equity, and sampling access are some of the factors that are typically considered in these decisions; however, other population and sewer system characteristics may be important to consider when optimizing WBS resources. In this study, we undertook WBS for SARS-CoV-2 (the virus that causes the COVID-19 disease) at six community sites located upstream of a large WWTP in the City of Toronto, Ontario, Canada. We then used mixed effects modelling to explore the dominant drivers of spatio-temporal variability in the relationship between the wastewater signal and clinical cases for SARS-CoV-2 across these sites. The data collected over a 17-month period suggested that population density, pipe length, and 'dependency' - a community marginalization index that quantifies the number of seniors, children, and adults whose work is not compensated - played a significant role in judging whether a specific site could be used as a sentinel site. Though the number of upstream community sites was relatively small - and there were correlations between predictors - the length of data record allowed us to demonstrate which variables had the strongest explanatory power in a multi-model context. Community marginalization indices can be used - in addition to physical variables like population density and sewer pipe length, to inform sentinel site selection for WBS in urban community 'sewersheds'.

摘要

基于废水的监测(WBS)能够检测出人群中疾病的存在,无论其症状状况如何,并为公共卫生干预措施提供支持,以减缓疾病传播。基于废水的监测可应用于各种空间尺度和人口规模,特别是在家庭由市政污水收集系统服务的地方(例如,由单个污水处理厂(WWTP)服务的大面积区域、单个社区内的较小区域、单个设施)。自2020年新冠疫情爆发以来,政府必须就何处以及在何种规模上实施基于废水的监测做出关键决策。人口规模、健康公平性和采样便利性是这些决策中通常会考虑的一些因素;然而,在优化基于废水的监测资源时,其他人口和下水道系统特征可能也很重要。在本研究中,我们在加拿大安大略省多伦多市一个大型污水处理厂上游的六个社区地点开展了针对严重急性呼吸综合征冠状病毒2(SARS-CoV-2,即导致新冠疾病的病毒)的基于废水的监测。然后,我们使用混合效应模型来探究这些地点中,废水信号与SARS-CoV-2临床病例之间关系的时空变异性的主要驱动因素。在17个月期间收集的数据表明,人口密度、管道长度和“依赖性”(一种社区边缘化指数,用于量化工作无报酬的老年人、儿童和成年人的数量)在判断特定地点是否可作为哨点方面发挥了重要作用。尽管上游社区地点的数量相对较少,且预测变量之间存在相关性,但数据记录的时长使我们能够证明在多模型背景下哪些变量具有最强的解释力。除了人口密度和下水道管道长度等物理变量外,社区边缘化指数还可用于为城市社区“排水流域”中基于废水的监测的哨点选择提供参考。

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