Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance

Evan P. Troendle; Andrew J. Lee; Marina I. Reyne; Danielle M. Allen; Stephen J. Bridgett; Clara H. Radulescu; Michael Glenn; John-Paul Wilkins; Francesco Rubino; Behnam Firoozi Nejad; Cormac McSparron; Marc Niebel; Derek J. Fairley; Kate E. Binley; Christopher J. Creevey; Jennifer M. McKinley; Timofey Skvortsov; Deirdre F. Gilpin; John W. McGrath; Connor G. G. Bamford; David A. Simpson

doi:10.1016/j.watres.2025.123953

Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance

Evan P. Troendle^*, Andrew J. Lee, Marina I. Reyne, Danielle M. Allen, Stephen J. Bridgett, Clara H. Radulescu, Michael Glenn, John-Paul Wilkins, Francesco Rubino, Behnam Firoozi Nejad, Cormac McSparron, Marc Niebel, Derek J. Fairley, Kate E. Binley, Christopher J. Creevey, Jennifer M. McKinley, Timofey Skvortsov, Deirdre F. Gilpin, John W. McGrath, Connor G. G. Bamford^*David A. Simpson^*

^*Corresponding author for this work

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Abstract

Effective pathogen surveillance is critical for public health decision-making, with both individual and environmental monitoring playing essential roles. While wastewater (WW) and individual whole genome sequencing (WGS) have been used to monitor SARS-CoV-2 dynamics, their complementary potential for enhancing national-level genomic surveillance remains underexplored. This study aimed to evaluate the unique and combined contributions of WW and individual WGS to genomic surveillance. We conducted SARS-CoV-2 WGS on over 4000 WW samples and 23,000 individual samples across Northern Ireland (NI) between 2021 and 2023. SARS-CoV-2 RNA was amplified using the ARTIC nCov-2019 and Mini-XT protocols and sequenced on Illumina MiSeq. Variant compositions in WW data were analysed using Freyja and compared to individual data using time series analysis, correlation assessments, and volatility measurements via numerical derivatives, with mean absolute error (MAE) calculations used to assess concordance. Wastewater treatment plants (WWTPs) were ranked by concordance to individual WGS data. WW and individual WGS complementarity was quantified by mutation classification and overlap analysis. Temporal curve shifting was used to identify lags or leads in variant detection and to infer differences in geospatial spread between WW and individual sequencing data. We confirmed strong concordance between WW and individual variant compositions (mean MAE = 6.2 %). MAE was inversely correlated with sequencing rate (Pearson r=−0.37, p < 0.001) and increased during periods with more circulating variants, highlighting the value of increased sequencing efforts during volatile periods. The population size served by a WWTP was not a reliable indicator of how well its variant composition matched that of the national individual sequencing programme. Both individual and WW-based sequencing (WBS) detected unique, as well as common mutations. Patterns of variant spread within NI were consistent between both programmes (Pearson r = 0.63, p = 0.036), providing complementary insights into variant trends and geospatial spread. We demonstrate that integration of individual and WW WGS data offers more comprehensive SARS-CoV-2 genomic surveillance and improves confidence in predictions of variant composition and spread.

Original language	English
Article number	123953
Number of pages	14
Journal	Water Research
Volume	284
Early online date	13 Jun 2025
DOIs	https://doi.org/10.1016/j.watres.2025.123953
Publication status	Early online date - 13 Jun 2025

Bibliographical note

Publisher Copyright:
© 2025 The Author(s)

Keywords

Bioinformatics
Genomic epidemiology for public health
Integrated surveillance systems
Sars-cov-2 variant detection
Wastewater sequencing
Whole genome sequencing (wgs)

ASJC Scopus subject areas

Environmental Engineering
Civil and Structural Engineering
Ecological Modelling
Water Science and Technology
Waste Management and Disposal
Pollution

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance
Copyright 2025 the authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 3.77 MBLicence: CC BY

Cite this

Troendle, E. P., Lee, A. J., Reyne, M. I., Allen, D. M., Bridgett, S. J., Radulescu, C. H., Glenn, M., Wilkins, J.-P., Rubino, F., Firoozi Nejad, B., McSparron, C., Niebel, M., Fairley, D. J., Binley, K. E., Creevey, C. J., McKinley, J. M., Skvortsov, T., Gilpin, D. F., McGrath, J. W., ... Simpson, D. A. (2025). Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance. Water Research, 284, Article 123953. Advance online publication. https://doi.org/10.1016/j.watres.2025.123953

@article{01f05fe9ecee4cc0b81ba58628d7e33d,

title = "Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance",

abstract = "Effective pathogen surveillance is critical for public health decision-making, with both individual and environmental monitoring playing essential roles. While wastewater (WW) and individual whole genome sequencing (WGS) have been used to monitor SARS-CoV-2 dynamics, their complementary potential for enhancing national-level genomic surveillance remains underexplored. This study aimed to evaluate the unique and combined contributions of WW and individual WGS to genomic surveillance. We conducted SARS-CoV-2 WGS on over 4000 WW samples and 23,000 individual samples across Northern Ireland (NI) between 2021 and 2023. SARS-CoV-2 RNA was amplified using the ARTIC nCov-2019 and Mini-XT protocols and sequenced on Illumina MiSeq. Variant compositions in WW data were analysed using Freyja and compared to individual data using time series analysis, correlation assessments, and volatility measurements via numerical derivatives, with mean absolute error (MAE) calculations used to assess concordance. Wastewater treatment plants (WWTPs) were ranked by concordance to individual WGS data. WW and individual WGS complementarity was quantified by mutation classification and overlap analysis. Temporal curve shifting was used to identify lags or leads in variant detection and to infer differences in geospatial spread between WW and individual sequencing data. We confirmed strong concordance between WW and individual variant compositions (mean MAE = 6.2 \%). MAE was inversely correlated with sequencing rate (Pearson r=−0.37, p < 0.001) and increased during periods with more circulating variants, highlighting the value of increased sequencing efforts during volatile periods. The population size served by a WWTP was not a reliable indicator of how well its variant composition matched that of the national individual sequencing programme. Both individual and WW-based sequencing (WBS) detected unique, as well as common mutations. Patterns of variant spread within NI were consistent between both programmes (Pearson r = 0.63, p = 0.036), providing complementary insights into variant trends and geospatial spread. We demonstrate that integration of individual and WW WGS data offers more comprehensive SARS-CoV-2 genomic surveillance and improves confidence in predictions of variant composition and spread.",

keywords = "Bioinformatics, Genomic epidemiology for public health, Integrated surveillance systems, Sars-cov-2 variant detection, Wastewater sequencing, Whole genome sequencing (wgs)",

author = "Troendle, \{Evan P.\} and Lee, \{Andrew J.\} and Reyne, \{Marina I.\} and Allen, \{Danielle M.\} and Bridgett, \{Stephen J.\} and Radulescu, \{Clara H.\} and Michael Glenn and John-Paul Wilkins and Francesco Rubino and \{Firoozi Nejad\}, Behnam and Cormac McSparron and Marc Niebel and Fairley, \{Derek J.\} and Binley, \{Kate E.\} and Creevey, \{Christopher J.\} and McKinley, \{Jennifer M.\} and Timofey Skvortsov and Gilpin, \{Deirdre F.\} and McGrath, \{John W.\} and Bamford, \{Connor G. G.\} and Simpson, \{David A.\}",

note = "Publisher Copyright: {\textcopyright} 2025 The Author(s)",

year = "2025",

month = jun,

day = "13",

doi = "10.1016/j.watres.2025.123953",

language = "English",

volume = "284",

journal = "Water Research",

issn = "1879-2448",

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Troendle, EP , Lee, AJ , Reyne, MI, Allen, DM, Bridgett, SJ , Radulescu, CH , Glenn, M , Wilkins, J-P, Rubino, F, Firoozi Nejad, B, McSparron, C, Niebel, M, Fairley, DJ, Binley, KE, Creevey, CJ , McKinley, JM , Skvortsov, T , Gilpin, DF , McGrath, JW , Bamford, CGG & Simpson, DA 2025, 'Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance', Water Research, vol. 284, 123953. https://doi.org/10.1016/j.watres.2025.123953

TY - JOUR

T1 - Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance

AU - Troendle, Evan P.

AU - Lee, Andrew J.

AU - Reyne, Marina I.

AU - Allen, Danielle M.

AU - Bridgett, Stephen J.

AU - Radulescu, Clara H.

AU - Glenn, Michael

AU - Wilkins, John-Paul

AU - Rubino, Francesco

AU - Firoozi Nejad, Behnam

AU - McSparron, Cormac

AU - Niebel, Marc

AU - Fairley, Derek J.

AU - Binley, Kate E.

AU - Creevey, Christopher J.

AU - McKinley, Jennifer M.

AU - Skvortsov, Timofey

AU - Gilpin, Deirdre F.

AU - McGrath, John W.

AU - Bamford, Connor G. G.

AU - Simpson, David A.

PY - 2025/6/13

Y1 - 2025/6/13

N2 - Effective pathogen surveillance is critical for public health decision-making, with both individual and environmental monitoring playing essential roles. While wastewater (WW) and individual whole genome sequencing (WGS) have been used to monitor SARS-CoV-2 dynamics, their complementary potential for enhancing national-level genomic surveillance remains underexplored. This study aimed to evaluate the unique and combined contributions of WW and individual WGS to genomic surveillance. We conducted SARS-CoV-2 WGS on over 4000 WW samples and 23,000 individual samples across Northern Ireland (NI) between 2021 and 2023. SARS-CoV-2 RNA was amplified using the ARTIC nCov-2019 and Mini-XT protocols and sequenced on Illumina MiSeq. Variant compositions in WW data were analysed using Freyja and compared to individual data using time series analysis, correlation assessments, and volatility measurements via numerical derivatives, with mean absolute error (MAE) calculations used to assess concordance. Wastewater treatment plants (WWTPs) were ranked by concordance to individual WGS data. WW and individual WGS complementarity was quantified by mutation classification and overlap analysis. Temporal curve shifting was used to identify lags or leads in variant detection and to infer differences in geospatial spread between WW and individual sequencing data. We confirmed strong concordance between WW and individual variant compositions (mean MAE = 6.2 %). MAE was inversely correlated with sequencing rate (Pearson r=−0.37, p < 0.001) and increased during periods with more circulating variants, highlighting the value of increased sequencing efforts during volatile periods. The population size served by a WWTP was not a reliable indicator of how well its variant composition matched that of the national individual sequencing programme. Both individual and WW-based sequencing (WBS) detected unique, as well as common mutations. Patterns of variant spread within NI were consistent between both programmes (Pearson r = 0.63, p = 0.036), providing complementary insights into variant trends and geospatial spread. We demonstrate that integration of individual and WW WGS data offers more comprehensive SARS-CoV-2 genomic surveillance and improves confidence in predictions of variant composition and spread.

AB - Effective pathogen surveillance is critical for public health decision-making, with both individual and environmental monitoring playing essential roles. While wastewater (WW) and individual whole genome sequencing (WGS) have been used to monitor SARS-CoV-2 dynamics, their complementary potential for enhancing national-level genomic surveillance remains underexplored. This study aimed to evaluate the unique and combined contributions of WW and individual WGS to genomic surveillance. We conducted SARS-CoV-2 WGS on over 4000 WW samples and 23,000 individual samples across Northern Ireland (NI) between 2021 and 2023. SARS-CoV-2 RNA was amplified using the ARTIC nCov-2019 and Mini-XT protocols and sequenced on Illumina MiSeq. Variant compositions in WW data were analysed using Freyja and compared to individual data using time series analysis, correlation assessments, and volatility measurements via numerical derivatives, with mean absolute error (MAE) calculations used to assess concordance. Wastewater treatment plants (WWTPs) were ranked by concordance to individual WGS data. WW and individual WGS complementarity was quantified by mutation classification and overlap analysis. Temporal curve shifting was used to identify lags or leads in variant detection and to infer differences in geospatial spread between WW and individual sequencing data. We confirmed strong concordance between WW and individual variant compositions (mean MAE = 6.2 %). MAE was inversely correlated with sequencing rate (Pearson r=−0.37, p < 0.001) and increased during periods with more circulating variants, highlighting the value of increased sequencing efforts during volatile periods. The population size served by a WWTP was not a reliable indicator of how well its variant composition matched that of the national individual sequencing programme. Both individual and WW-based sequencing (WBS) detected unique, as well as common mutations. Patterns of variant spread within NI were consistent between both programmes (Pearson r = 0.63, p = 0.036), providing complementary insights into variant trends and geospatial spread. We demonstrate that integration of individual and WW WGS data offers more comprehensive SARS-CoV-2 genomic surveillance and improves confidence in predictions of variant composition and spread.

KW - Bioinformatics

KW - Genomic epidemiology for public health

KW - Integrated surveillance systems

KW - Sars-cov-2 variant detection

KW - Wastewater sequencing

KW - Whole genome sequencing (wgs)

U2 - 10.1016/j.watres.2025.123953

DO - 10.1016/j.watres.2025.123953

M3 - Article

C2 - 40516408

SN - 1879-2448

VL - 284

JO - Water Research

JF - Water Research

M1 - 123953

ER -

Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this