Detecting SARS-CoV-2 RNA prone clusters in a municipal wastewater network using fuzzy-Bayesian optimization model to facilitate wastewater-based epidemiology

Abstract

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.

Data analytics during pandemics: a transportation and location planning perspective

“… 2021 ) Identifying target population clusters and optimizing locations for the implementation of wastewater-based epidemiology (Rallapalli et al. 2021 ) Forecasting the number of COVID-19 infections based on SARS-CoV-2 wastewater concentrations (Cao and Francis 2021 ) Infectious waste location-routing management for waste collection, transportation, treatment, and final disposal in Wuhan, China (Zhao et al. 2021 ) Analysis of waste management in Wuhan, China in terms of generation, transportation, and disposal of medical waste (Chen et al.…”

“…Identifying target population clusters and optimizing locations for the implementation of wastewater-based epidemiology (Rallapalli et al. 2021 )…”

“…( 2021 ) USA Cao and Francis ( 2021 ), Rallapalli et al. ( 2021 ) Germany Burgos and Ivanov ( 2021 ) Singapore Shah et al. ( 2021 ) Spain Calle et al.…”

“…( 2021 ), Cao and Francis ( 2021 ), Haider and Hafeez ( 2021 ), 5 Rallapalli et al. ( 2021 ), Shah et al. ( 2021 ) Medical waste transportation-routing Chen et al.…”

“…For example, bayesian analysis is proposed to identify targeted population clusters and optimized locations for implementation of wastewater-based epidemiology to detect COVID-19 (Rallapalli et al. 2021 ) and to analyze the impact of the COVID-19 epidemic on transportation systems (Bian et al. 2021 ; Zhang and Fricker 2021 ).…”