Department of Chemical Engineering, KTH Royal Institute of Technology, 10044 Stockholm, Sweden.
UNLOCK, Wageningen University & Research and Technical University Delft, 6708PB Wageningen, The Netherlands.
Int J Environ Res Public Health. 2023 Feb 26;20(5):4181. doi: 10.3390/ijerph20054181.
An approach based on wastewater epidemiology can be used to monitor the COVID-19 pandemic by assessing the gene copy number of SARS-CoV-2 in wastewater. In the present study, we statistically analyzed such data from six inlets of three wastewater treatment plants, covering six regions of Stockholm, Sweden, collected over an approximate year period (week 16 of 2020 to week 22 of 2021). SARS-CoV-2 gene copy number and population-based biomarker PMMoV, as well as clinical data, such as the number of positive cases, intensive care unit numbers, and deaths, were analyzed statistically using correlations and principal component analysis (PCA). Despite the population differences, the PCA for the Stockholm dataset showed that the case numbers are well grouped across wastewater treatment plants. Furthermore, when considering the data from the whole of Stockholm, the wastewater characteristics (flow rate m/day, PMMoV Ct value, and SARS-CoV gene copy number) were significantly correlated with the public health agency's report of SARS-CoV-2 infection rates (0.419 to 0.95, -value < 0.01). However, while the PCA results showed that the case numbers for each wastewater treatment plant were well grouped concerning PC1 (37.3%) and PC2 (19.67%), the results from the correlation analysis for the individual wastewater treatment plants showed varied trends. SARS-CoV-2 fluctuations can be accurately predicted through statistical analyses of wastewater-based epidemiology, as demonstrated in this study.
基于废水流行病学的方法可以通过评估废水中 SARS-CoV-2 的基因拷贝数来监测 COVID-19 大流行。在本研究中，我们对来自瑞典斯德哥尔摩六个地区的三个污水处理厂的六个入口的废水进行了统计分析，收集时间大约为一年（2020 年第 16 周至 2021 年第 22 周）。使用相关性和主成分分析（PCA）对 SARS-CoV-2 基因拷贝数和基于人群的生物标志物 PMMoV 以及临床数据（如阳性病例数、重症监护室数量和死亡人数）进行了统计分析。尽管存在人口差异，但斯德哥尔摩数据集的 PCA 表明，污水处理厂之间的病例数量分组情况良好。此外，当考虑整个斯德哥尔摩的数据时，废水特征（流量 m/day、PMMoV Ct 值和 SARS-CoV 基因拷贝数）与公共卫生机构报告的 SARS-CoV-2 感染率呈显著相关（0.419 至 0.95，-值<0.01）。然而，尽管 PCA 结果表明，每个污水处理厂的病例数量在 PC1（37.3%）和 PC2（19.67%）方面分组情况良好，但对各个污水处理厂的相关性分析结果显示出不同的趋势。本研究表明，通过基于废水的流行病学的统计分析可以准确预测 SARS-CoV-2 的波动。
