@article {47613, title = {Translating SARS-CoV-2 wastewater-based epidemiology for prioritizing mass vaccination: a strategic overview}, journal = {Environmental Science and Pollution Research}, volume = {28}, year = {2021}, month = {AUG}, pages = {42975-42980}, type = {Article}, abstract = {The inception of the novel coronavirus has forced the world into despair. Rapid progress has been made in addressing the situation, and various clinical diagnostic methods were developed for early detection of transmission. However, with a rapidly increasing number of infected populations worldwide, the testing of each individual was impractical. The wastewater-based epidemiology (WBE) has been implemented to evaluate disease outbreaks as an early warning system for pandemic preparedness. Numerous studies reported the presence of SARS-CoV-2 in the open drains and STPs across the globe via recovery efficiency of surrogate virus from existing virus concentration protocols. However, the such reported studies did not justify the use of WBE to identify or pinpoint the specific hotspots of transmission which could be prioritized for rapid efforts to contain or accelerate active vaccination efforts. Identifying precise locations of hotspots could be an essential aspect in controlling the outbreak and surge of wave by prioritizing the region for primary outbreak response. This article focuses on the issues relating to the primary focus for WBE that can be adapted, and its suitability for utilization in the mass vaccination program is discussed. Effective use of WBE information in terms of source tracking might be crucial as we move towards mass vaccination to control outbreaks of COVID-19 pandemic.}, keywords = {Epidemiology, Policy making, SARS-CoV-2, Vaccination, wastewater}, issn = {0944-1344}, doi = {10.1007/s11356-021-15169-7}, author = {Dharmadhikari, Tanmay and Yadav, Rakeshkumar and Dastager, Syed and Dharne, Mahesh} } @article {48236, title = {Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys}, journal = {Computers in Biology and Medicine}, volume = {146}, year = {2022}, month = {JUL}, pages = {105419}, type = {Article}, abstract = {

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8\% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6\%-80.2\%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

}, keywords = {BBV152, Covaxin, COVID-19, Ensemble methods, Infection, machine learning, SARS-CoV-2}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2022.105419}, author = {Singh, Prateek and Ujjainiya, Rajat and Prakash, Satyartha and Naushin, Salwa and Sardana, Viren and Bhatheja, Nitin and Singh, Ajay Pratap and Barman, Joydeb and Kumar, Kartik and Gayali, Saurabh and Khan, Raju and Rawat, Birendra Singh and Tallapaka, Karthik Bharadwaj and Anumalla, Mahesh and Lahiri, Amit and Kar, Susanta and Bhosale, Vivek and Srivastava, Mrigank and Mugale, Madhav Nilakanth and Pandey, C. P. and Khan, Shaziya and Katiyar, Shivani and Raj, Desh and Ishteyaque, Sharmeen and Khanka, Sonu and Rani, Ankita and Promila and Sharma, Jyotsna and Seth, Anuradha and Dutta, Mukul and Saurabh, Nishant and Veerapandian, Murugan and Venkatachalam, Ganesh and Bansal, Deepak and Gupta, Dinesh and Halami, Prakash M. and Peddha, Muthukumar Serva and Veeranna, Ravindra P. and Pal, Anirban and Singh, Ranvijay Kumar and Anandasadagopan, Suresh Kumar and Karuppanan, Parimala and Rahman, Syed Nasar and Selvakumar, Gopika and Venkatesan, Subramanian and Karmakar, Malay Kumar and Sardana, Harish Kumar and Kothari, Anamika and Parihar, Devendra Singh and Thakur, Anupma and Saifi, Anas and Gupta, Naman and Singh, Yogita and Reddu, Ritu and Gautam, Rizul and Mishra, Anuj and Mishra, Avinash and Gogeri, Iranna and Rayasam, Geethavani and Padwad, Yogendra and Patial, Vikram and Hallan, Vipin and Singh, Damanpreet and Tirpude, Narendra and Chakrabarti, Partha and Maity, Sujay Krishna and Ganguly, Dipyaman and Sistla, Ramakrishna and Balthu, Narender Kumar and Kumar, Kiran A. and Ranjith, Siva and Kumar, B. Vijay and Jamwal, Piyush Singh and Wali, Anshu and Ahmed, Sajad and Chouhan, Rekha and Gandhi, Sumit G. and Sharma, Nancy and Rai, Garima and Irshad, Faisal and Jamwal, Vijay Lakshmi and Paddar, Masroor Ahmad and Khan, Sameer Ullah and Malik, Fayaz and Ghosh, Debashish and Thakkar, Ghanshyam and Barik, S. K. and Tripathi, Prabhanshu and Satija, Yatendra Kumar and Mohanty, Sneha and Khan, Md Tauseef and Subudhi, Umakanta and Sen, Pradip and Kumar, Rashmi and Bhardwaj, Anshu and Gupta, Pawan and Sharma, Deepak and Tuli, Amit and Chaudhuri, Saumya Ray and Krishnamurthi, Srinivasan and Prakash, L. and Rao, V. Ch and Singh, B. N. and Chaurasiya, Arvindkumar and Chaurasiya, Meera and Bhadange, Mayuri and Likhitkar, Bhagyashree and Mohite, Sharada and Patil, Yogita and Kulkarni, Mahesh and Joshi, Rakesh and Pandya, Vaibhav and Mahajan, Sachin and Patil, Amita and Samson, Rachel and Vare, Tejas and Dharne, Mahesh and Giri, Ashok and Mahajan, Sachin and Paranjape, Shilpa and Sastry, G. Narahari and Kalita, Jatin and Phukan, Tridip and Manna, Prasenjit and Romi, Wahengbam and Bharali, Pankaj and Ozah, Dibyajyoti and Sahu, RaviKumar and Dutta, Prachurjya and Singh, Moirangthem Goutam and Gogoi, Gayatri and Tapadar, Yasmin Begam and Babu, Elapavalooru V. S. S. K. and Sukumaran, Rajeev K. and Nair, Aishwarya R. and Puthiyamadam, Anoop and Valappil, Prajeesh Kooloth and Prasannakumari, Adrash Velayudhan Pillai and Chodankar, Kalpana and Damare, Samir and Agrawal, Ved Varun and Chaudhary, Kumardeep and Agrawal, Anurag and Sengupta, Shantanu and Dash, Debasis} } @article {48489, title = {Comparative evaluation of advanced oxidation processes (AOPs) for reducing SARS-CoV-2 viral load from campus sewage water}, journal = {Journal of Environmental Chemical Engineering}, volume = {11}, year = {2023}, month = {JUN}, pages = {109673}, type = {Article}, abstract = {

Presence of SARS-CoV-2 in wastewater is a major concern as the wastewater meets rivers and other water bodies and is used by the population for various purposes. Hence it is very important to treat sewage water in an efficient manner in order to reduce the public health risk. In the present work, various advanced oxidation processes (AOPs) have been evaluated for disinfection of SARS-CoV-2 from sewage water collected from STP inlet of academic institutional residential. The sewage water was subjected to ten AOPs, which include Ozone (O3), Hydrodynamic cavitation (HC), Ultraviolet radiation (UV), and their hybrid combinations like HC/O3, HC/O3/ H2O2, HC/H2O2, O3/UV, UV/H2O2, UV/H2O2/O3, and O3/H2O2 to reduce SARS-CoV-2 viral load. Further, AOP treated sewage water was subjected to total nucleic acid isolation followed by RT-qPCR for viral load estimation. The sewage water treatment techniques were evaluated based on their viral concentration-reducing efficiency. It was found that ozone and ozone-coupled hybrid AOPs showed the most promising result with more than 98 \% SARS-CoV-2 viral load reducing efficiency from sewage water. Interestingly, the best six AOPs used in this study significantly reduced both the SARS-CoV-2 and PMMoV (faecal indicator) viral load and improved water quality in terms of increasing DO and decreasing TOC.

}, keywords = {AOPs and hybrid AOPs, Hydrodynamic cavitation, Ozonation, PMMoV, SARS-CoV-2, Sewage water treatment}, issn = {2213-2929}, doi = {10.1016/j.jece.2023.109673}, author = {Pramanik, Rinka and Bodawar, Narendra and Brahme, Aashay and Kamble, Sanjay and Dharne, Mahesh} } @article {48995, title = {Genomic surveillance reveals early detection and transition of delta to omicron lineages of SARS-CoV-2 variants in wastewater treatment plants of Pune, India}, journal = {Environmental Science and Pollution Research }, volume = {30}, year = {2023}, month = {DEC}, pages = {118976-118988}, type = {Article}, abstract = {

The COVID-19 pandemic has emphasized the urgency for rapid public health surveillance methods to detect and monitor the transmission of infectious diseases. The wastewater-based epidemiology (WBE) has emerged as a promising tool for proactive analysis and quantification of infectious pathogens within a population before clinical cases emerge. In the present study, we aimed to assess the trend and dynamics of SARS-CoV-2 variants using a longitudinal approach. Our objective included early detection and monitoring of these variants to enhance our understanding of their prevalence and potential impact. To achieve our goals, we conducted real-time quantitative polymerase chain reaction (RT-qPCR) and Illumina sequencing on 442 wastewater (WW) samples collected from 10 sewage treatment plants (STPs) in Pune city, India, spanning from November 2021 to April 2022. Our comprehensive analysis identified 426 distinct lineages representing 17 highly transmissible variants of SARS-CoV-2. Notably, fragments of Omicron variant were detected in WW samples prior to its first clinical detection in Botswana. Furthermore, we observed highly contagious sub-lineages of the Omicron variant, including BA.1 (similar to 28\%), BA.1.X (1.0-72\%), BA.2 (1.0-18\%), BA.2.X (1.0-97.4\%) BA.2.12 (0.8-0.25\%), BA.2.38 (0.8-1.0\%), BA.2.75 (0.01-0.02\%), BA.3 (0.09-6.3\%), BA.4 (0.24-0.29\%), and XBB (0.01-21.83\%), with varying prevalence rates. Overall, the present study demonstrated the practicality of WBE in the early detection of SARS-CoV-2 variants, which could help track future outbreaks of SARS-CoV-2. Such approaches could be implicated in monitoring infectious agents before they appear in clinical cases.

}, keywords = {Bioinformatics pipeline, COVID-19, Early warning, India, Next-generation sequencing, Omicron, Public health, SARS-CoV-2, wastewater, Wastewater-based epidemiology}, issn = {0944-1344}, doi = {10.1007/s11356-023-30709-z}, author = {Rajput, Vinay and Pramanik, Rinka and Malik, Vinita and Yadav, Rakeshkumar and Samson, Rachel and Kadam, Pradnya and Bhalerao, Unnati and Tupekar, Manisha and Deshpande, Dipti and Shah, Priyanki and Shashidhara, L. S. and Boargaonkar, Radhika and Patil, Dhawal and Kale, Saurabh and Bhalerao, Asim and Jain, Nidhi and Kamble, Sanjay and Dastager, Syed and Karmodiya, Krishanpal and Dharne, Mahesh} }