Edward Acheampong
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.
Authors
Aliabbas A. Husain
Hemanshi Dudani
Amit R. Nayak
Aditi Nag
Ekta Meena
Sandeep K. Shrivastava
PATRICK MCCLURE PATRICK.MCCLURE@NOTTINGHAM.AC.UK
Assistant Professor
ALEXANDER TARR alex.tarr@nottingham.ac.uk
Associate Professor
Dr COLIN CROOKS Colin.Crooks@nottingham.ac.uk
Clinical Associate Professor
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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Publisher Licence URL
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Copyright Statement
© 2024 Acheampong et al.
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