Wastewater SARS-CoV-2 monitoring as a community-level COVID-19 trend tracker and variants in Ohio, United States.
Wastewater SARS-CoV-2 monitoring as a community-level COVID-19 trend tracker and variants in Ohio, United States.
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DOI：
10.1016/j.scitotenv.2021.149757
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发表时间：
2021-12-20
期刊：
影响因子：
--
通讯作者：
Lee J
中科院分区：
文献类型：
--
作者：
Ai Y;Davis A;Jones D;Lemeshow S;Tu H;He F;Ru P;Pan X;Bohrerova Z;Lee J
由严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 引起的全球大流行已导致超过 1.29 亿例确诊病例。世界各地的许多卫生当局已经实施了基于废水的流行病学，作为 COVID-19 监测系统的快速和补充工具，最近还用于关注事件变体的跟踪。在本研究中，对 2020 年 7 月至 2021 年 1 月从俄亥俄州中部首府城市和其他 7 个不同规模城市获得的废水进水样本 (n = 250) 中的三个 SARS-CoV-2 目标基因（N1 和 N2 基因区域以及 E 基因）进行了定量。为了更准确地确定废水样本中人类特异性粪便强度，对两种人类粪便病毒（PMMoV 和 crAssphage）进行了定量 使废水中的 SARS-CoV-2 基因浓度正常化。为了从 SARS-CoV-2 基因水平估计新病例数的趋势，建立并评估了不同的统计模型。从纵向数据来看，废水中的 SARS-CoV-2 基因浓度与每日新确诊的 COVID-19 病例密切相关（平均 Spearman's r = 0.70，p < 0.05），其中 N2 基因区域是确诊病例趋势的最佳预测指标。此外，平均每日病例数有助于减少临床数据的噪音和变化。在测试的模型中，二次多项式模型在根据废水监测数据关联和预测 COVID-19 病例方面表现最佳，可用于跟踪大流行后期疫苗接种的有效性。有趣的是，使用 PMMoV 或 crAssphage 的归一化方法都没有显着增强与新病例数的相关性，也没有改进估计模型。病毒测序表明，废水样本中 SARS-CoV-2 菌株定义变异的变化与同一时期临床分离株中的变化相匹配。这项研究的结果表明，废水监测在跟踪 COVID-19 趋势方面是有效的，并为不同类型社区内变异的出现和传播提供哨兵警告。
The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 129 million confirm cases. Many health authorities around the world have implemented wastewater-based epidemiology as a rapid and complementary tool for the COVID-19 surveillance system and more recently for variants of concern emergence tracking. In this study, three SARS-CoV-2 target genes (N1 and N2 gene regions, and E gene) were quantified from wastewater influent samples (n = 250) obtained from the capital city and 7 other cities in various size in central Ohio from July 2020 to January 2021. To determine human-specific fecal strength in wastewater samples more accurately, two human fecal viruses (PMMoV and crAssphage) were quantified to normalize the SARS-CoV-2 gene concentrations in wastewater. To estimate the trend of new case numbers from SARS-CoV-2 gene levels, different statistical models were built and evaluated. From the longitudinal data, SARS-CoV-2 gene concentrations in wastewater strongly correlated with daily new confirmed COVID-19 cases (average Spearman's r = 0.70, p < 0.05), with the N2 gene region being the best predictor of the trend of confirmed cases. Moreover, average daily case numbers can help reduce the noise and variation from the clinical data. Among the models tested, the quadratic polynomial model performed best in correlating and predicting COVID-19 cases from the wastewater surveillance data, which can be used to track the effectiveness of vaccination in the later stage of the pandemic. Interestingly, neither of the normalization methods using PMMoV or crAssphage significantly enhanced the correlation with new case numbers, nor improved the estimation models. Viral sequencing showed that shifts in strain-defining variants of SARS-CoV-2 in wastewater samples matched those in clinical isolates from the same time periods. The findings from this study support that wastewater surveillance is effective in COVID-19 trend tracking and provide sentinel warning of variant emergence and transmission within various types of communities.
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DOI：
10.15585/mmwr.mm6915e3
发表时间：
2020-04-17
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
Garg S;Kim L;Whitaker M;O'Halloran A;Cummings C;Holstein R;Prill M;Chai SJ;Kirley PD;Alden NB;Kawasaki B;Yousey-Hindes K;Niccolai L;Anderson EJ;Openo KP;Weigel A;Monroe ML;Ryan P;Henderson J;Kim S;Como-Sabetti K;Lynfield R;Sosin D;Torres S;Muse A;Bennett NM;Billing L;Sutton M;West N;Schaffner W;Talbot HK;Aquino C;George A;Budd A;Brammer L;Langley G;Hall AJ;Fry A
通讯作者：
Fry A
影响因子：
7.5
作者：
Greaves J;Stone D;Wu Z;Bibby K
通讯作者：
Bibby K
影响因子：
82.9
作者：
He, Xi;Lau, Eric H. Y.;Leung, Gabriel M.
通讯作者：
Leung, Gabriel M.
影响因子：
4.4
作者：
Bae, Jinhee;Schwab, Kellogg J.
通讯作者：
Schwab, Kellogg J.
影响因子：
12.8
作者：
Hamza, Ibrahim Ahmed;Jurzik, Lars;Wilhelm, Michael
通讯作者：
Wilhelm, Michael