An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence

Mario Morvan; Anna Lo Jacomo; Celia Souque; Matthew J. Wade; Till Hoffmann; Koen Pouwels; Chris Lilley; Andrew C. Singer; Jonathan Porter; Nicholas P. Evens; David I. Walker; Joshua T. Bunce; Andrew Engeli; Jasmine Grimsley; Kathleen M. O’Reilly; Leon Danon

doi:10.1038/s41467-022-31753-y

An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence

Mario Morvan, Anna Lo Jacomo, Celia Souque, Matthew J. Wade, Till Hoffmann, Koen Pouwels, Chris Lilley, Andrew C. Singer, Jonathan Porter, Nicholas P. Evens, David I. Walker, Joshua T. Bunce, Andrew Engeli, Jasmine Grimsley, Kathleen M. O’Reilly^*, Leon Danon

^*Corresponding author for this work

Analytics and Data Sciences

Research output: Contribution to journal › Article › peer-review

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Abstract

Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Here, we use data from 45 sewage sites in England, covering 31% of the population, and estimate SARS-CoV-2 prevalence to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, we show that differences between sampled sites, particularly the wastewater flow rate, influence prevalence estimation and require careful interpretation. We find that SARS-CoV-2 signals in wastewater appear 4–5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that wastewater surveillance can be a leading indicator for symptomatic viral infections. Surveillance for viruses in wastewater complements and strengthens clinical surveillance, with significant implications for public health.

Original language	English
Article number	4313
Journal	Nature Communications
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41467-022-31753-y
Publication status	Published - Dec 2022

Bibliographical note

Funding Information:
We are grateful to Professor Rowland Kao, and Dr. Chris Jewell for helpful discussions in model development and interpretation, and for members of the “SPI-M” COVID-19 group for helpful feedback as the modelling developed. We are extremely grateful to representatives from the many water companies in England that provided insight into their sewage treatment works, and provided catchment area data for use in the analysis. This manuscript benefitted greatly from input provided during peer review. This study was predominantly funded by UKHSA, and KP acknowledges funding from UK Natural Environment Research Council National under the COVID-19 Wastewater Epidemiology Surveillance Programme (NE/V010441/1). The mapping within Fig. was facilitated through OpenStreetMap, under the Open Database License (CC BY-SA) ( https://www.openstreetmap.org/copyright ).

Publisher Copyright:
© 2022, Crown.

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Morvan, M., Jacomo, A. L., Souque, C., Wade, M. J., Hoffmann, T., Pouwels, K., Lilley, C., Singer, A. C., Porter, J., Evens, N. P., Walker, D. I., Bunce, J. T., Engeli, A., Grimsley, J., O’Reilly, K. M., & Danon, L. (2022). An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence. Nature Communications, 13(1), Article 4313. https://doi.org/10.1038/s41467-022-31753-y

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abstract = "Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Here, we use data from 45 sewage sites in England, covering 31% of the population, and estimate SARS-CoV-2 prevalence to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, we show that differences between sampled sites, particularly the wastewater flow rate, influence prevalence estimation and require careful interpretation. We find that SARS-CoV-2 signals in wastewater appear 4–5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that wastewater surveillance can be a leading indicator for symptomatic viral infections. Surveillance for viruses in wastewater complements and strengthens clinical surveillance, with significant implications for public health.",

author = "Mario Morvan and Jacomo, {Anna Lo} and Celia Souque and Wade, {Matthew J.} and Till Hoffmann and Koen Pouwels and Chris Lilley and Singer, {Andrew C.} and Jonathan Porter and Evens, {Nicholas P.} and Walker, {David I.} and Bunce, {Joshua T.} and Andrew Engeli and Jasmine Grimsley and O{\textquoteright}Reilly, {Kathleen M.} and Leon Danon",

note = "Funding Information: We are grateful to Professor Rowland Kao, and Dr. Chris Jewell for helpful discussions in model development and interpretation, and for members of the “SPI-M” COVID-19 group for helpful feedback as the modelling developed. We are extremely grateful to representatives from the many water companies in England that provided insight into their sewage treatment works, and provided catchment area data for use in the analysis. This manuscript benefitted greatly from input provided during peer review. This study was predominantly funded by UKHSA, and KP acknowledges funding from UK Natural Environment Research Council National under the COVID-19 Wastewater Epidemiology Surveillance Programme (NE/V010441/1). The mapping within Fig. was facilitated through OpenStreetMap, under the Open Database License (CC BY-SA) ( https://www.openstreetmap.org/copyright ). Publisher Copyright: {\textcopyright} 2022, Crown.",

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Morvan, M, Jacomo, AL, Souque, C, Wade, MJ, Hoffmann, T, Pouwels, K, Lilley, C, Singer, AC, Porter, J, Evens, NP, Walker, DI, Bunce, JT, Engeli, A, Grimsley, J, O’Reilly, KM & Danon, L 2022, 'An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence', Nature Communications, vol. 13, no. 1, 4313. https://doi.org/10.1038/s41467-022-31753-y

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N2 - Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Here, we use data from 45 sewage sites in England, covering 31% of the population, and estimate SARS-CoV-2 prevalence to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, we show that differences between sampled sites, particularly the wastewater flow rate, influence prevalence estimation and require careful interpretation. We find that SARS-CoV-2 signals in wastewater appear 4–5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that wastewater surveillance can be a leading indicator for symptomatic viral infections. Surveillance for viruses in wastewater complements and strengthens clinical surveillance, with significant implications for public health.

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An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence

Abstract

Bibliographical note

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