Background

The application of wastewater-based epidemiology to track the outbreak and prevalence of coronavirus disease (COVID-19) in communities has been tested and validated by several researchers across the globe. However, the RNA-based surveillance has its inherent limitations and uncertainties.

Objective

This study aims to complement the ongoing wastewater surveillance efforts by analyzing other chemical biomarkers in wastewater to help assess community response (hospitalization and treatment) during the pandemic (2020–2021).

Methods

Wastewater samples (n = 183) were collected from the largest wastewater treatment facility in Suffolk County, NY, USA and analyzed for COVID-19 treatment drugs (remdesivir, chloroquine, and hydroxychloroquine (HCQ)) and their human metabolites. We additionally monitored 26 pharmaceuticals including common over-the-counter (OTC) drugs. Lastly, we developed a Bayesian model that uses viral RNA, COVID-19 treatment drugs, and pharmaceuticals data to predict the confirmed COVID-19 cases within the catchment area.

Results

The viral RNA levels in wastewater tracked the actual COVID-19 case numbers well as expected. COVID-19 treatment drugs were detected with varying frequency (9–100%) partly due to their instability in wastewater. We observed a significant correlation (R = 0.30, p < 0.01) between the SARS-CoV-2 genes and desethylhydroxychloroquine (DHCQ, metabolite of HCQ). Remdesivir levels peaked immediately after the Emergency Use Authorization approved by the FDA. Although, 13 out of 26 pharmaceuticals assessed were consistently detected (DF = 100%, n = 111), only acetaminophen was significantly correlated with viral loads, especially when the Omicron variant was dominant. The Bayesian models were capable of reproducing the temporal trend of the confirmed cases.

Impact

In this study, for the first time, we measured COVID-19 treatment and pharmaceutical drugs and their metabolites in wastewater to complement ongoing COVID-19 viral RNA surveillance efforts. Our results highlighted that, although the COVID-19 treatment drugs were not very stable in wastewater, their detection matched with usage trends in the community. Acetaminophen, an OTC drug, was significantly correlated with viral loads and confirmed cases, especially when the Omicron variant was dominant. A Bayesian model was developed which could predict COVID-19 cases more accurately when incorporating other drugs data along with viral RNA levels in wastewater.

中文翻译：

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对非处方药 (OTC) 和 COVID-19 治疗药物的监测补充了 SARS-CoV-2 废水监测

背景

应用基于废水的流行病学来追踪社区冠状病毒病 (COVID-19) 的爆发和流行情况已经过全球几位研究人员的测试和验证。然而，基于RNA的监测有其固有的局限性和不确定性。

客观的

本研究旨在通过分析废水中的其他化学生物标志物来补充正在进行的废水监测工作，以帮助评估大流行期间（2020-2021 年）的社区反应（住院和治疗）。

方法

 从美国纽约州萨福克县最大的废水处理设施收集了废水样本 ( n = 183)，并分析了 COVID-19 治疗药物（瑞德西韦、氯喹和羟氯喹 (HCQ)）及其人体代谢物。我们还监测了 26 种药物，包括常见的非处方 (OTC) 药物。最后，我们开发了一个贝叶斯模型，该模型使用病毒 RNA、COVID-19 治疗药物和药品数据来预测流域内确诊的 COVID-19 病例。

结果

废水中的病毒 RNA 水平与实际的 COVID-19 病例数相符，符合预期。COVID-19 治疗药物的检测频率各不相同（9-100%），部分原因是它们在废水中不稳定。我们观察 到 SARS-CoV-2 基因和去乙基羟氯喹（DHCQ，HCQ 的代谢物）之间存在显着相关性（ R  = 0.30，p < 0.01）。在 FDA 批准紧急使用授权后，瑞德西韦的水平立即达到峰值。尽管评估的 26 种药物中有 13 种得到了一致检测（DF = 100%，n  = 111），但只有对乙酰氨基酚与病毒载量显着相关，特别是当 Omicron 变体占主导地位时。贝叶斯模型能够再现确诊病例的时间趋势。

影响

在这项研究中，我们首次测量了废水中的 COVID-19 治疗和药物及其代谢物，以补充正在进行的 COVID-19 病毒 RNA 监测工作。我们的结果强调，尽管 COVID-19 治疗药物在废水中不太稳定，但其检测结果与社区的使用趋势相符。对乙酰氨基酚是一种非处方药，与病毒载量和确诊病例显着相关，特别是当 Omicron 变异占主导地位时。开发了一种贝叶斯模型，将其他药物数据与废水中的病毒 RNA 水平结合起来，可以更准确地预测 COVID-19 病例。