Rapid, Large-Scale Wastewater Surveillance and Automated Reporting System Enable Early Detection of Nearly 85% of COVID-19 Cases on a University Campus.
Rapid, Large-Scale Wastewater Surveillance and Automated Reporting System Enable Early Detection of Nearly 85% of COVID-19 Cases on a University Campus.
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DOI：
10.1128/msystems.00793-21
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发表时间：
2021-08-31
期刊：
影响因子：
6.4
通讯作者：
Knight R
中科院分区：
文献类型：
--
作者：
Karthikeyan S;Nguyen A;McDonald D;Zong Y;Ronquillo N;Ren J;Zou J;Farmer S;Humphrey G;Henderson D;Javidi T;Messer K;Anderson C;Schooley R;Martin NK;Knight R
由于其成本效益和为早期公共卫生干预提供信息的能力，基于水的监测已经成为预测COVID-19（2019年冠状病毒病）感染动态的领先指标，并获得了突出地位。大学校园尤其可以从废水监测中受益，因为大学的特点是基本上没有症状的人群，是潜在的传播热点，需要经常进行诊断检测。在这项研究中，我们采用了一个大规模的GIS（地理信息系统），使建筑水平的废水监测系统与7,614个人的校园住宅。68个自动废水采样器被部署到监测239个校园建筑，重点是住宅建筑。每天收集时间加权复合样本，并在同一天进行分析。通过自动化大大简化了样本处理，将周转时间缩短了20倍，并超过类似监测项目的规模10至100倍，从而克服了废水监测中的最大瓶颈之一。开发了一个自动废水通知系统，以提醒居民与其住所相关的阳性废水样本，并鼓励学生免费接受校园提供的无症状检测。该系统与加州大学圣地亚哥分校（UC San Diego）“返回学习”项目的其他部分相结合，导致校园内近85%的COVID-19病例得到早期诊断。在废水通知后，COVID-19检测率增加了1.9倍至13倍。我们的研究表明，随着大学校园和其他高风险环境的重新开放，一个强大、高效的废水监测系统有可能大大降低感染风险。重要性-基于水的流行病学在大学校园中特别有价值，在良好控制的背景下进行高分辨率空间采样不仅可以深入了解影响校园社区的因素，还可以将这些推断扩展到更广泛的城市/县背景。在本研究中，在一所大型大学校园成功实施了大规模的废水监测，使85%的COVID-19病例得以早期发现，从而避免了潜在的疫情爆发。高度自动化的样品处理到报告系统使96个样品的周转时间大幅缩短至5小时（样品到结果的时间）。此外，样品处理管道的小型化显著降低了处理成本（13美元/样品）。总之，这些结果表明，这样一个系统可以大大改善对这些社区的长期监测，因为他们希望重新开放。作者视频：本文的作者视频摘要可用。
Wastewater-based surveillance has gained prominence and come to the forefront as a leading indicator of forecasting COVID-19 (coronavirus disease 2019) infection dynamics owing to its cost-effectiveness and its ability to inform early public health interventions. A university campus could especially benefit from wastewater surveillance, as universities are characterized by largely asymptomatic populations and are potential hot spots for transmission that necessitate frequent diagnostic testing. In this study, we employed a large-scale GIS (geographic information systems)-enabled building-level wastewater monitoring system associated with the on-campus residences of 7,614 individuals. Sixty-eight automated wastewater samplers were deployed to monitor 239 campus buildings with a focus on residential buildings. Time-weighted composite samples were collected on a daily basis and analyzed on the same day. Sample processing was streamlined significantly through automation, reducing the turnaround time by 20-fold and exceeding the scale of similar surveillance programs by 10- to 100-fold, thereby overcoming one of the biggest bottlenecks in wastewater surveillance. An automated wastewater notification system was developed to alert residents to a positive wastewater sample associated with their residence and to encourage uptake of campus-provided asymptomatic testing at no charge. This system, integrated with the rest of the “Return to Learn” program at the University of California (UC) San Diego-led to the early diagnosis of nearly 85% of all COVID-19 cases on campus. COVID-19 testing rates increased by 1.9 to 13× following wastewater notifications. Our study shows the potential for a robust, efficient wastewater surveillance system to greatly reduce infection risk as college campuses and other high-risk environments reopen. IMPORTANCE Wastewater-based epidemiology can be particularly valuable at university campuses where high-resolution spatial sampling in a well-controlled context could not only provide insight into what affects campus community as well as how those inferences can be extended to a broader city/county context. In the present study, a large-scale wastewater surveillance was successfully implemented on a large university campus enabling early detection of 85% of COVID-19 cases thereby averting potential outbreaks. The highly automated sample processing to reporting system enabled dramatic reduction in the turnaround time to 5 h (sample to result time) for 96 samples. Furthermore, miniaturization of the sample processing pipeline brought down the processing cost significantly ($13/sample). Taken together, these results show that such a system could greatly ameliorate long-term surveillance on such communities as they look to reopen. Author Video: An author video summary of this article is available.