Sharma Puru Dutt, Rallapalli Srinivas, Lakkaniga Naga Rajiv
Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan India.
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Twin Cities, Minneapolis, MN USA.
Stoch Environ Res Risk Assess. 2023 May 19:1-18. doi: 10.1007/s00477-023-02468-3.
Early prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations in big populations because it cannot deliver information at a fast enough rate to stop the spread in its early phases. Wastewater based epidemiology (WBE) experiments showed promising results for brisk detection of 'SARS CoV-2' RNA in urban wastewater. However, a systematic and targeted approach to track COVID-19 virus in the complex wastewater networks at a community level is lacking. This research combines graph network (GN) theory with fuzzy logic to determine the chances of a specific community being a COVID-19 hotspot in a wastewater network. To detect 'SARS-CoV-2' RNA, GN divides wastewater network into communities and fuzzy logic-based inference system is used to identify targeted communities. For the propose of tracking, 4000 sample cases from Minnesota (USA) were tested based on various contributing factors. With a probability score of greater than 0.8, 42% of cases were likely to be designated as COVID-19 hotspots based on multiple demographic characteristics. The research enhances the conventional WBE approach through two novel aspects, viz. (1) by integrating graph theory with fuzzy logic for quick prediction of potential hotspot along with its likelihood percentage in a wastewater network, and (2) incorporating the uncertainty associated with COVID-19 contributing factors using fuzzy membership functions. The targeted approach allows for rapid testing and implementation of vaccination campaigns in potential hotspots. Consequently, governmental bodies can be well prepared to check future pandemics and variant spreading in a more planned manner.
The online version contains supplementary material available at 10.1007/s00477-023-02468-3.
对新冠病毒感染社区（潜在热点地区）进行早期预测对于限制病毒传播至关重要。诊断检测在大规模人群中存在局限性，因为它无法以足够快的速度提供信息以在早期阶段阻止传播。基于废水的流行病学（WBE）实验在城市废水中快速检测“严重急性呼吸综合征冠状病毒2”（SARS-CoV-2）RNA方面显示出有前景的结果。然而，在社区层面的复杂废水网络中缺乏一种系统且有针对性的方法来追踪新冠病毒。本研究将图网络（GN）理论与模糊逻辑相结合，以确定特定社区成为废水网络中新冠热点地区的可能性。为了检测SARS-CoV-2 RNA，GN将废水网络划分为多个社区，并使用基于模糊逻辑的推理系统来识别目标社区。为了进行追踪，基于各种影响因素对来自美国明尼苏达州的4000个样本案例进行了测试。基于多种人口统计学特征，概率得分大于0.8时，42%的案例可能被指定为新冠热点地区。该研究通过两个新的方面改进了传统的WBE方法，即：（1）通过将图论与模糊逻辑相结合，快速预测潜在热点地区及其在废水网络中的可能性百分比；（2）使用模糊隶属函数纳入与新冠病毒影响因素相关的不确定性。这种有针对性的方法允许在潜在热点地区快速进行检测和开展疫苗接种运动。因此，政府机构能够更好地做好准备，以更有计划的方式应对未来的大流行和变异毒株传播。
在线版本包含可在doi:10.1007/s00477-023-02468-3获取的补充材料。
