Fraunhofer Institute for Industrial Mathematics, 67663, Kaiserslautern, Germany.
Sci Rep. 2024 Jun 22;14(1):14384. doi: 10.1038/s41598-024-64864-1.
Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland-Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland-Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland-Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ( ) and a delay ( days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.
基于污水的流行病学已成为监测不同病原体浓度趋势的广泛应用工具，尤其是 SARS-CoV-2 等广泛存在的病原体。因此，2022 年，莱茵兰-普法尔茨州的科学和卫生部也将 16 个污水处理厂纳入了一个监测计划，该计划每两周提供一次样本。然而，仅病毒载量数据会受到强烈波动的影响，并且其本身对于政治决策者的价值有限。因此，莱茵兰-普法尔茨州委托美因茨约翰内斯古腾堡大学医学中心开展了一项名为 SentiSurv 的代表性队列研究，该研究中有越来越多的参与者（多达 12000 人）每周使用敏感的抗原自我检测一次或两次来检测自己是否感染 SARS-CoV-2 并报告自己的状态。这使莱茵兰-普法尔茨州处于拥有污水中病毒载量和人群中 SARS-CoV-2 流行率的时间序列的有利地位。我们的主要贡献是基于 2023-01-08 至 2023-10-01 期间的数据进行的校准研究，我们确定了一个缩放因子 ( ) 和一个滞后 ( 天) ，即污水中经辣椒轻斑驳病毒 (PMMoV) 归一化的病毒载量与 SentiSurv 研究中记录的流行率之间的滞后。通过将流行病学模型拟合到两个时间序列，建立了这种关系。我们展示了当队列数据不再可用时如何使用它来估计流行率，以及如何在提前几周将其用作预测工具。我们表明，校准和预测质量以及由此产生的因素强烈取决于如何对污水样本进行归一化。
