First wastewater surveillance-based city zonation for effective COVID-19 pandemic preparedness powered by early warning: A study of Ahmedabad, India

Kumar, M.; Joshi, M.; Shah, A. V.; Srivastava, V.; Dave, S.

Following the proven concept, capabilities, and limitations of detecting the RNA of Severe Acute Respiratory Coronavirus 2 (SARS-CoV-2) in wastewater, it is pertinent to understand the utility of wastewater surveillance data on various scale. In the present work, we put forward the first wastewater surveillance-based city zonation for effective COVID-19 pan-demic preparedness. A three-month data of Surveillance of Wastewater for Early Epidemic Prediction (SWEEP) was generated for the world heritage city of Ahmedabad, Gujarat, India. In this expedition, one hundred sixteen wastewater samples were analyzed to detect SARS-CoV-2 RNA, from September 3rd to November 26th, 2020. A total of 111 samples were detected with at least two out of three SARS-CoV-2 genes (N, ORF 1ab, and S). Monthly variation depicted a significant decline in all three genes copies in October compared to September 2020, followed by a sharp increment in November 2020. Correspondingly, the descending order of average genome concentration was: November (~10729 copies/ L) > September (~3047 copies/ L) > October (~454 copies/ L). Monthly variation of SARS-CoV-2 RNA in the wastewater samples may be ascribed to a decline of 19.3% in the total number of active cases in October 2020 and a rise of 1.82% in November 2020. Also, the monthly recovery rate of patients was 16.61, 19.31, and 15.58% in September, October, and Novem-ber 2020, respectively. The percentage change in the genome concentration was observed in the lead of 1-2 weeks with respect to the provisional figures of confirmed cases. SWEEP data-based city zonation was matched with the heat map of the overall COVID-19 infected population in Ahmedabad city, and month-wise effective RNA concentration variations are shown on the map. The results expound on the potential of WBE surveillance of COVID-19 as a city zonation tool that can be meaningfully interpreted, predicted, and propagated for community preparedness through advance identification of COVID-19 hotspots within a given city.