DataSheet1_Leveraging environmental microbial indicators in wastewater for data-driven disease diagnostics.docx

IntroductionWastewater-based surveillance (WBS) is an emerging tool for monitoring the spread of infectious diseases, such as SARS-CoV-2, in community settings. Environmental factors, including water quality parameters and seasonal variations, may influence the prevalence of viral particles in wastewater. This study aims to explore the relationships between these factors and the incidence of SARS-CoV-2 across 28 monitoring sites, spanning different seasons and water strata.
MethodsSamples were collected from 28 sites, accounting for seasonal and spatial (surface and intermediate water layers) variations. Key physicochemical parameters, heavy metals, and minerals were measured, and viral presence was detected using RT-qPCR. After data preprocessing, correlation analyses identified 19 relevant environmental parameters. Unsupervised learning algorithms, including K-means and K-medoid clustering, were employed to categorize the data into four distinct clusters, revealing patterns of viral positivity and environmental conditions.
ResultsCluster analysis indicated that seasonal variations and water quality characteristics significantly influenced SARS-CoV-2 positivity rates. The four clusters demonstrated distinct associations between environmental factors and viral prevalence, with certain clusters correlating with higher viral loads in specific seasons. The clustering patterns varied across sample sites, reflecting the diverse environmental conditions and their influence on viral detection.
DiscussionThe findings underscore the critical role of environmental factors, such as water quality and seasonality, in shaping the dynamics of SARS-CoV-2 prevalence in wastewater. These insights provide a deeper understanding of the complex interplay between environmental contexts and disease spread. By utilizing WBS and advanced data analysis techniques, this study offers a robust framework for future research aimed at enhancing public health surveillance and interventions.

引言
基于废水的监测（Wastewater-based surveillance, WBS）是一种新兴的社区传染病传播监测手段，可用于监测如严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）等病原体的社区传播情况。包括水质参数与季节变化在内的环境因素，可能会影响废水中病毒颗粒的流行水平。本研究旨在覆盖28个监测点位，涵盖不同季节与不同水层，探究上述环境因素与SARS-CoV-2感染发病率之间的关联。

方法
本研究共采集28个点位的样本，覆盖季节与空间（表层水与中层水）维度的变异。研究测定了关键理化参数、重金属与矿物质含量，并通过逆转录实时荧光定量聚合酶链反应（RT-qPCR）检测病毒的存在情况。完成数据预处理后，通过相关性分析筛选出19项相关环境参数。本研究采用无监督学习算法（包括K-means聚类与K-medoid聚类）将数据划分为4个不同的簇，从而揭示病毒阳性率与环境条件之间的关联模式。

结果
聚类分析结果显示，季节波动与水质特征对SARS-CoV-2阳性检出率具有显著影响。4个簇分别展现出环境因素与病毒流行率之间的独特关联，部分簇在特定季节呈现出更高的病毒载量。不同采样点位的聚类模式存在差异，反映出环境条件的多样性及其对病毒检测结果的影响。

讨论
本研究结果证实，水质与季节波动等环境因素在调控废水中SARS-CoV-2流行动态方面发挥着关键作用。这些发现有助于更深入地理解环境背景与疾病传播之间的复杂相互作用。本研究通过采用基于废水的监测（WBS）与先进数据分析技术，为未来旨在强化公共卫生监测与干预措施的研究提供了一套可靠的研究框架。