Department of Civil and Environmental Engineering, Michigan State University, 1449 Engineering Research Ct., East Lansing, MI 48823, USA.
Department of Civil and Environmental Engineering, Michigan State University, 1449 Engineering Research Ct., East Lansing, MI 48823, USA.
Sci Total Environ. 2024 Oct 1;945:174140. doi: 10.1016/j.scitotenv.2024.174140. Epub 2024 Jun 19.
To monitor COVID-19 through wastewater surveillance, global researchers dedicated significant endeavors and resources to develop and implement diverse RT-qPCR or RT-ddPCR assays targeting different genes of SARS-CoV-2. Effective wastewater surveillance hinges on the appropriate selection of the most suitable assay, especially for resource-constrained regions where scant technical and socioeconomic resources restrict the options for testing with multiple assays. Further research is imperative to evaluate the existing assays through comprehensive comparative analyses. Such analyses are crucial for health agencies and wastewater surveillance practitioners in the selection of appropriate methods for monitoring COVID-19. In this study, untreated wastewater samples were collected weekly from the Detroit wastewater treatment plant, Michigan, USA, between January and December 2023. Polyethylene glycol precipitation (PEG) was applied to concentrate the samples followed by RNA extraction and RT-ddPCR. Three assays including N1, N2 (US CDC Real-Time Reverse Transcription PCR Panel for Detection of SARS-CoV-2), and SC2 assay (US CDC Influenza SARS-CoV-2 Multiplex Assay) were implemented to detect SARS-CoV-2 in wastewater. The limit of blank and limit of detection for the three assays were experimentally determined. SARS-CoV-2 RNA concentrations were evaluated and compared through three statistical approaches, including Pearson and Spearman's rank correlations, Dynamic Time Warping, and vector autoregressive models. N1 and N2 demonstrated the highest correlation and most similar time series patterns. Conversely, N2 and SC2 assay demonstrated the lowest correlation and least similar time series patterns. N2 was identified as the optimal target to predict COVID-19 cases. This study presents a rigorous effort in evaluating and comparing SARS-CoV-2 RNA concentrations quantified with N1, N2, and SC2 assays and their interrelations and correlations with clinical cases. This study provides valuable insights into identifying the optimal target for monitoring COVID-19 through wastewater surveillance.
为了通过污水监测来监控 COVID-19，全球研究人员投入了大量精力和资源，开发并实施了多种针对 SARS-CoV-2 不同基因的 RT-qPCR 或 RT-ddPCR 检测方法。有效的污水监测取决于对最合适检测方法的恰当选择，特别是在资源有限的地区，那里技术和社会经济资源匮乏，限制了使用多种方法进行检测的选择。需要进一步研究通过全面的比较分析来评估现有的检测方法。这些分析对于卫生机构和污水监测从业者在选择监测 COVID-19 的适当方法方面至关重要。在这项研究中，从美国密歇根州底特律污水处理厂每周收集一次未经处理的污水样本，时间为 2023 年 1 月至 12 月。采用聚乙二醇沉淀（PEG）浓缩样品，然后进行 RNA 提取和 RT-ddPCR。实施了三种检测方法，包括 N1、N2（美国疾病预防控制中心实时逆转录 PCR 检测 SARS-CoV-2 试剂盒）和 SC2 检测法（美国疾病预防控制中心流感 SARS-CoV-2 多重检测试剂盒），以检测污水中的 SARS-CoV-2。通过实验确定了三种检测方法的空白极限和检测极限。通过三种统计方法评估和比较了 SARS-CoV-2 RNA 浓度，包括 Pearson 和 Spearman 等级相关、动态时间规整和向量自回归模型。N1 和 N2 表现出最高的相关性和最相似的时间序列模式。相反，N2 和 SC2 检测法表现出最低的相关性和最不相似的时间序列模式。N2 被确定为预测 COVID-19 病例的最佳靶标。本研究通过 N1、N2 和 SC2 检测法评估和比较了 SARS-CoV-2 RNA 浓度，并评估了它们与临床病例的相互关系和相关性，这是一项严谨的努力。本研究为通过污水监测来识别监测 COVID-19 的最佳靶标提供了有价值的见解。
