High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County.
High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County.
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DOI：
10.1128/msystems.00045-21
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发表时间：
2021-03-02
期刊：
影响因子：
6.4
通讯作者：
Knight R
中科院分区：
文献类型：
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作者：
Karthikeyan S;Ronquillo N;Belda-Ferre P;Alvarado D;Javidi T;Longhurst CA;Knight R
大规模废水监测能够极大地增强对感染动态的跟踪，特别是在流行率远远超过检测能力的社区。然而，目前废水中病毒检测的方法在扩大规模以实现高通量方面严重缺乏。在本研究中，我们采用基于磁珠的自动化浓缩方法来检测污水中的病毒，该方法可以有效地扩大规模，在 40 分钟的单次运行中处理 24 个样本。与传统使用的病毒废水浓缩方法相比，该方法具有更高的回收效率，输入样品量低至 10ml，一天可处理 100 多个废水样品。高通量方案的灵敏度显示，可在 415 名居民的大楼中检测到 1 名无症状个体。使用高通量管道，对圣地亚哥县初级废水处理厂（为 230 万居民提供服务）的进水流样本进行了 13 周的处理。未经处理的原始废水中严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 病毒基因组拷贝的废水估计值与该县临床报告的病例密切相关，当与自回归综合移动平均 (ARIMA) 模型中过去报告的病例数和时间信息一起使用时，可以提前 3 周预测新报告的病例。总而言之，结果表明，高通量监测可以通过提供可靠、快速的估计来极大地改善全面的社区患病率评估。重要性 废水监测对于在 2019 年冠状病毒病 (COVID-19) 爆发之前揭示其爆发具有很大潜力，因为在人们出现临床症状之前就在废水中发现了该病毒。然而，基于废水的监测的应用受到较长处理时间的限制，特别是在浓缩步骤。在这里，我们介绍了一种更快的样本处理方法，并通过与现有方法的直接比较来展示其稳健性，并表明我们可以使用城市污水以极好的准确度预测圣地亚哥的病例一周，并以相当的准确度预测三周。自动病毒浓缩方法将通过减少流行病期间的周转时间来极大地缓解废水处理的主要瓶颈。
Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead-based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-min run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10 ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect 1 asymptomatic individual in a building of 415 residents. Using the high-throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego County (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates. IMPORTANCE Wastewater monitoring has a lot of potential for revealing coronavirus disease 2019 (COVID-19) outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples and show its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and 3 weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics.