Rallapalli Srinivas, Aggarwal Shubham, Singh Ajit Pratap
Birla Institute of Technology and Science, Pilani, Rajasthan, India; Department of Bioproducts and Biosystems Engineering, University of Minnesota, USA.
Birla Institute of Technology and Science, Pilani, Rajasthan, India.
Sci Total Environ. 2021 Jul 15;778:146294. doi: 10.1016/j.scitotenv.2021.146294. Epub 2021 Mar 8.
The current pandemic disease coronavirus (COVID-19) has not only become a worldwide health emergency, but also devoured the global economy. Despite appreciable research, identification of targeted populations for testing and tracking the spread of COVID-19 at a larger scale is an intimidating challenge. There is a need to quickly identify the infected individual or community to check the spread. The diagnostic testing done at large-scale for individuals has limitations as it cannot provide information at a swift pace in large populations, which is pivotal to contain the spread at the early stage of its breakouts. Recently, scientists are exploring the presence of SARS-CoV-2 RNA in the faeces discharged in municipal wastewater. Wastewater sampling could be a potential tool to expedite the early identification of infected communities by detecting the biomarkers from the virus. However, it needs a targeted approach to choose optimized locations for wastewater sampling. The present study proposes a novel fuzzy based Bayesian model to identify targeted populations and optimized locations with a maximum probability of detecting SARS-CoV-2 RNA in wastewater networks. Consequently, real time monitoring of SARS-CoV-2 RNA in wastewater using autosamplers or biosensors could be deployed efficiently. Fourteen criteria such as population density, patients with comorbidity, quarantine and hospital facilities, etc. are analysed using the data of 14 lac individuals infected by COVID-19 in the USA. The uniqueness of the proposed model is its ability to deal with the uncertainty associated with the data and decision maker's opinions using fuzzy logic, which is fused with Bayesian approach. The evidence-based virus detection in wastewater not only facilitates focused testing, but also provides potential communities for vaccine distribution. Consequently, governments can reduce lockdown periods, thereby relieving human stress and boosting economic growth.
当前的大流行性疾病冠状病毒（COVID-19）不仅已成为全球卫生突发事件，还吞噬了全球经济。尽管进行了大量研究，但确定大规模检测的目标人群以及追踪COVID-19的传播仍是一项艰巨的挑战。需要迅速识别受感染的个体或社区以控制传播。对个体进行的大规模诊断检测存在局限性，因为它无法在大量人群中快速提供信息，而这对于在疫情爆发的早期阶段控制传播至关重要。最近，科学家们正在探索城市污水排放物中是否存在严重急性呼吸综合征冠状病毒2（SARS-CoV-2）RNA。污水采样可能是一种潜在工具，通过检测病毒的生物标志物来加快对受感染社区的早期识别。然而，它需要一种有针对性的方法来选择污水采样的优化地点。本研究提出了一种基于模糊的新型贝叶斯模型，以识别目标人群和优化地点，从而在污水管网中检测SARS-CoV-2 RNA的概率最大。因此，可以有效地部署使用自动采样器或生物传感器对污水中的SARS-CoV-2 RNA进行实时监测。利用美国140万感染COVID-19的个体数据，分析了人口密度、合并症患者、隔离和医院设施等14项标准。所提出模型的独特之处在于其能够使用模糊逻辑处理与数据和决策者意见相关的不确定性，并将其与贝叶斯方法融合。基于证据的污水中病毒检测不仅有助于有针对性的检测，还为疫苗分发提供了潜在社区。因此，政府可以缩短封锁期，从而减轻人类压力并促进经济增长。
