Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, United States of America; Bioinformatics Research Center, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, United States of America.
Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, United States of America.
Sci Total Environ. 2021 Aug 15;782:146749. doi: 10.1016/j.scitotenv.2021.146749. Epub 2021 Mar 30.
The COVID-19 pandemic has been a source of ongoing challenges and presents an increased risk of illness in group environments, including jails, long-term care facilities, schools, and residential college campuses. Early reports that the SARS-CoV-2 virus was detectable in wastewater in advance of confirmed cases sparked widespread interest in wastewater-based epidemiology (WBE) as a tool for mitigation of COVID-19 outbreaks. One hypothesis was that wastewater surveillance might provide a cost-effective alternative to other more expensive approaches such as pooled and random testing of groups. In this paper, we report the outcomes of a wastewater surveillance pilot program at the University of North Carolina at Charlotte, a large urban university with a substantial population of students living in on-campus dormitories. Surveillance was conducted at the building level on a thrice-weekly schedule throughout the university's fall residential semester. In multiple cases, wastewater surveillance enabled the identification of asymptomatic COVID-19 cases that were not detected by other components of the campus monitoring program, which also included in-house contact tracing, symptomatic testing, scheduled testing of student athletes, and daily symptom reporting. In the context of all cluster events reported to the University community during the fall semester, wastewater-based testing events resulted in the identification of smaller clusters than were reported in other types of cluster events. Wastewater surveillance was able to detect single asymptomatic individuals in dorms with resident populations of 150-200. While the strategy described was developed for COVID-19, it is likely to be applicable to mitigation of future pandemics in universities and other group-living environments.
COVID-19 大流行一直是持续挑战的源头，并在群体环境中增加了患病的风险，包括监狱、长期护理机构、学校和住宿大学校园。早期有报道称，SARS-CoV-2 病毒在确诊病例之前可在废水中检测到，这引发了人们对基于废水的流行病学（WBE）作为减轻 COVID-19 爆发的工具的广泛兴趣。一种假设是，废水监测可能提供一种具有成本效益的替代方案，而不是其他更昂贵的方法，例如对群体进行 pooled 和随机测试。在本文中，我们报告了北卡罗来纳大学夏洛特分校废水监测试点计划的结果，该校是一所拥有大量学生居住在校园宿舍的大型城市大学。在整个大学秋季住宿学期，按照每周三次的时间表在建筑物级别进行监测。在多个案例中，废水监测能够识别无症状 COVID-19 病例，这些病例未被校园监测计划的其他部分（包括内部接触者追踪、症状检测、学生运动员定期检测和每日症状报告）检测到。在秋季学期向大学社区报告的所有集群事件中，基于废水的检测事件确定的集群比其他类型的集群事件报告的集群要小。废水监测能够检测到 150-200 名居民宿舍中无症状的个人。虽然所描述的策略是为 COVID-19 开发的，但它很可能适用于减轻未来大学和其他群体居住环境中的大流行。
