Swift Candice L, Isanovic Mirza, Correa Velez Karlen E, Norman R Sean
Department of Environmental Health Sciences, University of South Carolina, 921 Assembly Street, Suite 401, Columbia, SC 29208, USA.
Environ Adv. 2023 Apr;11:100347. doi: 10.1016/j.envadv.2023.100347. Epub 2023 Jan 25.
Wastewater surveillance of SARS-CoV-2 has proven instrumental in mitigating the spread of COVID-19 by providing an economical and equitable approach to disease surveillance. Here, we analyze the correlation of SARS-CoV-2 RNA in influents of seven wastewater plants (WWTPs) across the state of South Carolina with corresponding daily case counts to determine whether underlying characteristics of WWTPs and sewershed populations predict stronger correlations. The populations served by these WWTPs have varying social vulnerability and represent 24% of the South Carolina population. The study spanned 15 months from April 19, 2020, to July 1, 2021, which includes the administration of the first COVID-19 vaccines. SARS-CoV-2 RNA concentrations were measured by either reverse transcription quantitative PCR (RT-qPCR) or droplet digital PCR (RT-ddPCR). Although populations served and average flow rate varied across WWTPs, the strongest correlation was identified for six of the seven WWTPs when daily case counts were lagged two days after the measured SARS-CoV-2 RNA concentration in wastewater. The weakest correlation was found for WWTP 6, which had the lowest ratio of population served to average flow rate, indicating that the SARS-CoV-2 signal was too dilute for a robust correlation. Smoothing daily case counts by a 7-day moving average improved correlation strength between case counts and SARS-CoV-2 RNA concentration in wastewater while dampening the effect of lag-time optimization. Correlation strength between cases and SARS-CoV-2 RNA was compared for cases determined at the ZIP-code and sewershed levels. The strength of correlations using ZIP-code-level versus sewershed-level cases were not statistically different across WWTPs. Results indicate that wastewater surveillance, even without normalization to fecal indicators, is a strong predictor of clinical cases by at least two days, especially when SARS-CoV-2 RNA is measured using RT-ddPCR. Furthermore, the ratio of population served to flow rate may be a useful metric to assess whether a WWTP is suitable for a surveillance program.
对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的废水监测已证明有助于通过提供一种经济且公平的疾病监测方法来减轻新冠病毒病（COVID-19）的传播。在此，我们分析了南卡罗来纳州七个污水处理厂（WWTPs）进水口中SARS-CoV-2核糖核酸（RNA）与相应每日病例数之间的相关性，以确定污水处理厂和排水区域人口的潜在特征是否能预测更强的相关性。这些污水处理厂服务的人群具有不同的社会脆弱性，占南卡罗来纳州人口的24%。该研究从2020年4月19日至2021年7月1日，历时15个月，其中包括首批新冠病毒疫苗的接种。SARS-CoV-2 RNA浓度通过逆转录定量聚合酶链反应（RT-qPCR）或数字液滴聚合酶链反应（RT-ddPCR）进行测量。尽管各污水处理厂服务的人群和平均流速各不相同，但在废水样本中测得的SARS-CoV-2 RNA浓度之后，将每日病例数滞后两天时，七个污水处理厂中的六个显示出最强的相关性。污水处理厂6的相关性最弱，其服务人口与平均流速之比最低，这表明SARS-CoV-2信号过于稀释，无法形成稳健的相关性。通过7天移动平均法对每日病例数进行平滑处理，提高了病例数与废水中SARS-CoV-2 RNA浓度之间的相关强度，同时减弱了滞后时间优化的影响。比较了在邮政编码和排水区域层面确定的病例与SARS-CoV-2 RNA之间的相关强度。在各污水处理厂中，使用邮政编码层面病例与使用排水区域层面病例的相关强度在统计学上并无差异。结果表明，即使不进行粪便指标归一化处理，废水监测也能至少提前两天有力地预测临床病例，尤其是在使用RT-ddPCR测量SARS-CoV-2 RNA时。此外，服务人口与流速之比可能是评估一个污水处理厂是否适合监测计划的有用指标。
