Gogoi Gayatri, Singh Sarangthem Dinamani, Koch Devpratim, Kalyan Emon, Boro Rashmi Rani, Devi Aradhana, Mahanta Hridoy Jyoti, Bharali Pankaj
Centre for Infectious Diseases, Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology, Jorhat, India.
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Front Bioeng Biotechnol. 2024 Nov 25;12:1508964. doi: 10.3389/fbioe.2024.1508964. eCollection 2024.
Wastewater-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.
Samples 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.
Cluster 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.
The 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.
基于废水的监测(WBS)是一种新兴工具,用于监测社区环境中传染病(如严重急性呼吸综合征冠状病毒2,SARS-CoV-2)的传播。包括水质参数和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情况。本研究旨在探讨这些因素与28个监测点SARS-CoV-2发病率之间的关系,这些监测点跨越不同季节和水层。
从28个地点采集样本,考虑到季节和空间(地表水层和中间水层)变化。测量关键理化参数、重金属和矿物质,并使用逆转录定量聚合酶链反应(RT-qPCR)检测病毒的存在。经过数据预处理后,相关性分析确定了19个相关环境参数。采用包括K均值聚类和K中心点聚类在内的无监督学习算法,将数据分类为四个不同的聚类,揭示病毒阳性和环境条件的模式。
聚类分析表明,季节变化和水质特征显著影响SARS-CoV-2阳性率。这四个聚类显示了环境因素与病毒流行率之间的不同关联,某些聚类在特定季节与较高的病毒载量相关。聚类模式因采样地点而异,反映了不同的环境条件及其对病毒检测的影响。
研究结果强调了水质和季节性等环境因素在塑造废水中SARS-CoV-2流行动态方面的关键作用。这些见解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。通过利用基于废水的监测和先进的数据分析技术,本研究为未来旨在加强公共卫生监测和干预的研究提供了一个强大的框架。