Monitoring of SARS-CoV-2 Variants by Wastewater-Based Surveillance as a Sustainable and Pragmatic Approach—A Case Study of Jaipur (India)

Wastewater-based surveillance has been emerging as an efficient and advantageous tool to predict COVID-19 prevalence in the population, much earlier (7–28 days) than reported clinical cases, thus providing sufficient time to organize resources and optimize their use in managing COVID-19. Since the commencement of the COVID-19 pandemic, SARS-CoV-2 genetic lineages have emerged and are circulating all over the world. The assessment of SARS-CoV-2 variants of concern (VOCs) in wastewater has recently been proven to be successful. The present research demonstrates a case study utilizing an established approach to perform monitoring of SARS-CoV-2 variants from 11 distinct wastewater treatment plants across Jaipur (India) during the second peak period of COVID-19 (from 19 February 2021 to 8 June 2021). The sequences obtained were analyzed to detect lineage using the Pangolin tool and SNPs using the mpileup utility of Samtools, which reported high genome coverage. The mutation analyses successfully identified the penetration of the B.1. in the first two weeks of sampling (19–26 February), followed by the B.1.617.2 variant into Jaipur in the first week of March 2021. B.1.617.2 was initially discovered in India in October 2020; however, it was not reported until early April 2021.The present study identified the presence of B.1.617.2 in early March, which correlates well with the clinical patient’s data (290 cases were reported much later by the government on 10 May 2021). The average total genome coverage of the samples is 94.39% when mapped onto the severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1; a complete genome (NC_045512.2) sequence and SNP analysis showed that 37–51 SNPs were identified in each sample. The current study demonstrates that sewage surveillance for variant characterization is a reliable and practical method for tracking the diversity of SARS-CoV-2 strains in the community that is considerably faster than clinical genomic surveillance. As a result, this method can predict the advent of epidemiologically or clinically important mutations/variants, which can help with public health decision making.