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Population infection estimation from wastewater surveillance for SARS-CoV-2 in Nagpur, India during the second pandemic wave

Acheampong, Edward; Husain, Aliabbas A.; Dudani, Hemanshi; Nayak, Amit R.; Nag, Aditi; Meena, Ekta; Shrivastava, Sandeep K.; McClure, Patrick; Tarr, Alexander W.; Crooks, Colin; Lade, Ranjana; Gomes, Rachel L.; Singer, Andrew; Kumar, Saravana; Bhatnagar, Tarun; Arora, Sudipti; Kashyap, Rajpal Singh; Monaghan, Tanya M.

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Authors

Edward Acheampong

Aliabbas A. Husain

Hemanshi Dudani

Amit R. Nayak

Aditi Nag

Ekta Meena

Sandeep K. Shrivastava

Ranjana Lade

RACHEL GOMES rachel.gomes@nottingham.ac.uk
Professor of Water & Resource Processing

Andrew Singer

Saravana Kumar

Tarun Bhatnagar

Sudipti Arora

Rajpal Singh Kashyap

TANYA MONAGHAN Tanya.Monaghan@nottingham.ac.uk
Clinical Associate Professor in Luminal Gastroenterology



Contributors

Ricardo Santos
Editor

Abstract

Wastewater-based epidemiology (WBE) has emerged as an effective environmental surveillance tool for predicting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease outbreaks in high-income countries (HICs) with centralized sewage infrastructure. However, few studies have applied WBE alongside epidemic disease modelling to estimate the prevalence of SARS-CoV-2 in low-resource settings. This study aimed to explore the feasibility of collecting untreated wastewater samples from rural and urban catchment areas of Nagpur district, to detect and quantify SARS-CoV-2 using real-time qPCR, to compare geographic differences in viral loads, and to integrate the wastewater data into a modified Susceptible-Exposed-Infectious-Confirmed Positives-Recovered (SEIPR) model. Of the 983 wastewater samples analyzed for SARS-CoV-2 RNA, we detected significantly higher sample positivity rates, 43.7% (95% confidence interval (CI) 40.1, 47.4) and 30.4% (95% CI 24.66, 36.66), and higher viral loads for the urban compared with rural samples, respectively. The Basic reproductive number, R0, positively correlated with population density and negatively correlated with humidity, a proxy for rainfall and dilution of waste in the sewers. The SEIPR model estimated the rate of unreported coronavirus disease 2019 (COVID-19) cases at the start of the wave as 13.97 [95% CI (10.17, 17.0)] times that of confirmed cases, representing a material difference in cases and healthcare resource burden. Wastewater surveillance might prove to be a more reliable way to prepare for surges in COVID-19 cases during future waves for authorities.

Citation

Acheampong, E., Husain, A. A., Dudani, H., Nayak, A. R., Nag, A., Meena, E., …Monaghan, T. M. (2024). Population infection estimation from wastewater surveillance for SARS-CoV-2 in Nagpur, India during the second pandemic wave. PLoS ONE, 19(5), Article e0303529. https://doi.org/10.1371/journal.pone.0303529

Journal Article Type Article
Acceptance Date Apr 26, 2024
Online Publication Date May 29, 2024
Publication Date May 29, 2024
Deposit Date Apr 30, 2024
Publicly Available Date May 29, 2024
Journal PLoS ONE
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 19
Issue 5
Article Number e0303529
DOI https://doi.org/10.1371/journal.pone.0303529
Public URL https://nottingham-repository.worktribe.com/output/34332061

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