Development of neural network models by innovatively expanding conventional WBE dataset for the monitoring of variability of COVID-19, variants of SARS-CoV-2, and antidrug resistance in four major cities of India
Implementing Organization
University of Petroleum and Energy Studies
Principal Investigator
Dr. Manish Kumar
University of Petroleum and Energy Studies
CO-Principal Investigator
Dr. Madhvi Nandlal Joshi
Gujarat Biotechnology Research Centre (GBRC), Gandhinagar, Gujarat-382011
CO-Principal Investigator
Dr. Bhumika Jayantkumar Prajapati
Gujarat Biotechnology Research Centre (GBRC), Gandhinagar, Gujarat-382011
CO-Principal Investigator
Dr. Vaibhav Srivastava
University of Petroleum and Energy Studies
About
The global COVID-19 pandemic, caused by SARS-CoV-2 infection, has spread in 216 countries and territories, with over 423 million confirmed cases and 5,878,328 deaths as of February 20, 2022. A significant portion of infections are undiagnosed, with about half being asymptomatic. Wastewater-based genomic epidemiology (WBGE) is an effective approach for early warning of outbreaks and quarantine of undiagnosed infections. The emergence of new variants with higher infectivity has increased the threat of COVID-19. Detection of SARS-CoV-2 variants in wastewater samples is a promising strategy for control. WBGE aims to detect and quantify SARS-CoV-2 genetic material in urban waters of Gandhinagar, Ahmedabad, Dehradun, and Delhi, develop an online portal with weekly genome concentration updates, predict the risk of SARS-CoV-2 in natural water bodies using a quantitative microbial risk assessment (QMRA) approach, identify new circulating SARS-CoV-2 variants in wastewater via genome sequencing, analyze the comparative status of antidrug resistance in E. coli isolated from urban waters, and assist authorities and policymakers in developing and improving COVID-19 surveillance to gain a clear picture of the pandemic.
Patents
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Outcome / Output
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Source
Source
Science and Engineering Research Board (SERB), DST 2022-23