ABSTRACT
Environmental surveillance (ES) of a pathogen is crucial for understanding the community load of disease. As an early warning system, ES for SARS-CoV-2 has complemented routine diagnostic surveillance by capturing near real-time virus circulation at a population level. In this longitudinal study in 28 sewershed sites in Bangalore city, we quantified SARS-CoV-2 RNA to track infection dynamics and provide evidence of change in the relative abundance of emerging variants. We describe an early warning system using the exponentially weighted moving average control chart and demonstrate how SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 8-14 days earlier in wastewater than in clinical data. This was further corroborated by showing that the estimated number of infections is strongly correlated with SARS-CoV-2 RNA copies detected in the wastewater. Using a deconvolution matrix, we detected emerging variants of concern up to two months earlier in wastewater samples. In addition, we found a huge diversity in variants detected in wastewater compared to clinical samples. Our study highlights that quantifying viral titres, correlating it with a known number of cases in the area, and combined with genomic surveillance helps in tracking VOCs over time and space, enabling timely and making informed policy decisions.