背景:已经实施了基于废水的流行病学(WBE)来监测COVID-19的激增。然而,多种因素阻碍了WBE的有用性,可能需要进行定量调整。
目的:我们旨在建立WBE数据与COVID-19病例之间的关系模型,同时调整混杂因素和自相关因素。
方法:这项全国性的WBE研究包括比利时40个污水处理厂(WWTP)的数据(02/2021-06/2022)。我们应用基于ARIMA的建模来评估每日流量的影响,辣椒轻度斑驳病毒(PMMoV)浓度,测量废水中的人类粪便,和变体(阿尔法,delta,和omicron菌株)对废水中SARS-CoV-2RNA水平的影响。其次,在不同滞后时间调整后的WBE指标用于预测COVID-19事件。模型选择基于AICc最小化。
结果:在33/40个污水处理厂中,RNA水平最好用事件来解释,流量,PMMoV。流速和PMMoV与每SD增加的RNA水平变化-13.0%(95%预测区间:-26.1至+0.2%)和+13.0%(95%预测区间:+5.1至+21.0%)相关,分别。在38/40污水处理厂中,变异不能解释独立于病例的RNA水平的变异性.此外,我们的研究表明,在15/40个WWTP中,RNA水平可导致事件发生至少一周.领先的WWTP的中位人口规模比非领先的WWTP的中位人口规模大85.1%。在17/40污水处理厂中,然而,除了自相关外,RNA水平并不能导致或解释事件。
结论:这项研究为WBE的关键决定因素提供了定量见解,包括废水流量的影响,PMMoV,和变体。在解释事件案例方面,观察到了WWTP之间的实质性变化。这些发现对WBE从业人员具有实际重要性,并表明WBE的预警潜力是针对WWTP的,需要进行验证。
BACKGROUND: Wastewater-based epidemiology (WBE) has been implemented to monitor surges of COVID-19. Yet, multiple factors impede the usefulness of WBE and quantitative adjustment may be required.
OBJECTIVE: We aimed to model the relationship between WBE data and incident COVID-19 cases, while adjusting for confounders and autocorrelation.
METHODS: This nationwide WBE
study includes data from 40 wastewater treatment plants (WWTPs) in Belgium (02/2021-06/2022). We applied ARIMA-based modelling to assess the effect of daily flow rate, pepper mild mottle virus (PMMoV) concentration, a measure of human faeces in wastewater, and variants (alpha, delta, and omicron strains) on SARS-CoV-2 RNA levels in wastewater. Secondly, adjusted WBE metrics at different lag times were used to predict incident COVID-19 cases. Model selection was based on AICc minimization.
RESULTS: In 33/40 WWTPs, RNA levels were best explained by incident cases, flow rate, and PMMoV. Flow rate and PMMoV were associated with -13.0 % (95 % prediction interval: -26.1 to +0.2 %) and +13.0 % (95 % prediction interval: +5.1 to +21.0 %) change in RNA levels per SD increase, respectively. In 38/40 WWTPs, variants did not explain variability in RNA levels independent of cases. Furthermore, our
study shows that RNA levels can lead incident cases by at least one week in 15/40 WWTPs. The median population size of leading WWTPs was 85.1 % larger than that of non‑leading WWTPs. In 17/40 WWTPs, however, RNA levels did not lead or explain incident cases in addition to autocorrelation.
CONCLUSIONS: This
study provides quantitative insights into key determinants of WBE, including the effects of wastewater flow rate, PMMoV, and variants. Substantial inter-WWTP variability was observed in terms of explaining incident cases. These findings are of practical importance to WBE practitioners and show that the early-warning potential of WBE is WWTP-specific and needs validation.