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A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

Guangquan Li, Hubert Denise Orcid Logo, Peter Diggle, Jasmine Grimsley, Chris Holmes, Daniel James, Radka Jersakova, Callum Mole Orcid Logo, George Nicholson Orcid Logo, Camila Rangel Smith, Sylvia Richardson Orcid Logo, William Rowe Orcid Logo, Barry Rowlingson, Fatemeh Torabi Orcid Logo, Matthew J. Wade Orcid Logo, Marta Blangiardo Orcid Logo

Environment International, Volume: 172, Start page: 107765

Swansea University Author: Fatemeh Torabi Orcid Logo

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DOI (Published version): 10.1016/j.envint.2023.107765

Abstract

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted t...

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Published in: Environment International
ISSN: 0160-4120
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa71654
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Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks.We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation.We evaluate the model&#x2019;s predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks).The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.</abstract><type>Journal Article</type><journal>Environment International</journal><volume>172</volume><journalNumber/><paginationStart>107765</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0160-4120</issnPrint><issnElectronic/><keywords>SARS-CoV-2; Wastewater viral concentration; Bayesian spatio-temporal model; Spatial prediction; Probabilistic detection</keywords><publishedDay>1</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-02-01</publishedDate><doi>10.1016/j.envint.2023.107765</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>The United Kingdom Government (Department of Health and Social Care) funded the sampling, testing, and data analysis of wastewater in England via the UK Health Security Agency&#x2019;s Environmental Monitoring for Health Protection National Surveillance programme. SR is supported by MRC programme grant MC_UU_00002/10; The Alan Turing Institute grant: TU/B/000092; EPSRC Bayes4Health programme grant: EP/R018561/1. GN and CH acknowledge support from the Medical Research Council Programme Leaders award MC_UP_A390_1107. CH acknowledges support from The Alan Turing Institute, Health Data Research, U.K., and the Engineering and Physical Sciences Research Council through the Bayes4Health programme grant: EP/R018561/1. MB acknowledges partial support from the MRC Centre for Environment and Health, which is currently funded by the Medical Research Council (MR/S019669/1). FT acknowledges support from the MRC grant MR/V028367/1, the HDR-9006, the ESRC ES/S007393/1 and the Wales COVID-19 Evidence Centre. Infrastructure support for the Department of Epidemiology and Biostatistics is also provided by the NIHR Imperial BRC. Authors in The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory gratefully acknowledge funding from Data, Analytics and Surveillance Group, a part of the UKHSA. This work was funded by The Department for Health and Social Care with in-kind support from The Alan Turing Institute and The Royal Statistical Society.</funders><projectreference/><lastEdited>2026-04-20T15:33:54.2606703</lastEdited><Created>2026-03-19T23:25:02.8755119</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Guangquan</firstname><surname>Li</surname><order>1</order></author><author><firstname>Hubert</firstname><surname>Denise</surname><orcid>0000-0001-9862-5890</orcid><order>2</order></author><author><firstname>Peter</firstname><surname>Diggle</surname><order>3</order></author><author><firstname>Jasmine</firstname><surname>Grimsley</surname><order>4</order></author><author><firstname>Chris</firstname><surname>Holmes</surname><order>5</order></author><author><firstname>Daniel</firstname><surname>James</surname><order>6</order></author><author><firstname>Radka</firstname><surname>Jersakova</surname><order>7</order></author><author><firstname>Callum</firstname><surname>Mole</surname><orcid>0000-0002-1463-6419</orcid><order>8</order></author><author><firstname>George</firstname><surname>Nicholson</surname><orcid>0000-0001-9588-6075</orcid><order>9</order></author><author><firstname>Camila Rangel</firstname><surname>Smith</surname><order>10</order></author><author><firstname>Sylvia</firstname><surname>Richardson</surname><orcid>0000-0003-1998-492x</orcid><order>11</order></author><author><firstname>William</firstname><surname>Rowe</surname><orcid>0000-0002-4983-2495</orcid><order>12</order></author><author><firstname>Barry</firstname><surname>Rowlingson</surname><order>13</order></author><author><firstname>Fatemeh</firstname><surname>Torabi</surname><orcid>0000-0002-5853-4625</orcid><order>14</order></author><author><firstname>Matthew J.</firstname><surname>Wade</surname><orcid>0000-0001-9824-7121</orcid><order>15</order></author><author><firstname>Marta</firstname><surname>Blangiardo</surname><orcid>0000-0002-1621-704x</orcid><order>16</order></author></authors><documents><document><filename>71654__36537__b052f02bc4d84f9d918bae9bf947b834.pdf</filename><originalFilename>71654.VoR.pdf</originalFilename><uploaded>2026-04-20T15:28:36.5429757</uploaded><type>Output</type><contentLength>6581882</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9;2023 The Authors. 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spelling 2026-04-20T15:33:54.2606703 v2 71654 2026-03-19 A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic f569591e1bfb0e405b8091f99fec45d3 0000-0002-5853-4625 Fatemeh Torabi Fatemeh Torabi true false 2026-03-19 MEDS The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks.We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation.We evaluate the model’s predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks).The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance. Journal Article Environment International 172 107765 Elsevier BV 0160-4120 SARS-CoV-2; Wastewater viral concentration; Bayesian spatio-temporal model; Spatial prediction; Probabilistic detection 1 2 2023 2023-02-01 10.1016/j.envint.2023.107765 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Another institution paid the OA fee The United Kingdom Government (Department of Health and Social Care) funded the sampling, testing, and data analysis of wastewater in England via the UK Health Security Agency’s Environmental Monitoring for Health Protection National Surveillance programme. SR is supported by MRC programme grant MC_UU_00002/10; The Alan Turing Institute grant: TU/B/000092; EPSRC Bayes4Health programme grant: EP/R018561/1. GN and CH acknowledge support from the Medical Research Council Programme Leaders award MC_UP_A390_1107. CH acknowledges support from The Alan Turing Institute, Health Data Research, U.K., and the Engineering and Physical Sciences Research Council through the Bayes4Health programme grant: EP/R018561/1. MB acknowledges partial support from the MRC Centre for Environment and Health, which is currently funded by the Medical Research Council (MR/S019669/1). FT acknowledges support from the MRC grant MR/V028367/1, the HDR-9006, the ESRC ES/S007393/1 and the Wales COVID-19 Evidence Centre. Infrastructure support for the Department of Epidemiology and Biostatistics is also provided by the NIHR Imperial BRC. Authors in The Alan Turing Institute and Royal Statistical Society Statistical Modelling and Machine Learning Laboratory gratefully acknowledge funding from Data, Analytics and Surveillance Group, a part of the UKHSA. This work was funded by The Department for Health and Social Care with in-kind support from The Alan Turing Institute and The Royal Statistical Society. 2026-04-20T15:33:54.2606703 2026-03-19T23:25:02.8755119 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Guangquan Li 1 Hubert Denise 0000-0001-9862-5890 2 Peter Diggle 3 Jasmine Grimsley 4 Chris Holmes 5 Daniel James 6 Radka Jersakova 7 Callum Mole 0000-0002-1463-6419 8 George Nicholson 0000-0001-9588-6075 9 Camila Rangel Smith 10 Sylvia Richardson 0000-0003-1998-492x 11 William Rowe 0000-0002-4983-2495 12 Barry Rowlingson 13 Fatemeh Torabi 0000-0002-5853-4625 14 Matthew J. Wade 0000-0001-9824-7121 15 Marta Blangiardo 0000-0002-1621-704x 16 71654__36537__b052f02bc4d84f9d918bae9bf947b834.pdf 71654.VoR.pdf 2026-04-20T15:28:36.5429757 Output 6581882 application/pdf Version of Record true ©2023 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
spellingShingle A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
Fatemeh Torabi
title_short A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
title_full A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
title_fullStr A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
title_full_unstemmed A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
title_sort A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
author_id_str_mv f569591e1bfb0e405b8091f99fec45d3
author_id_fullname_str_mv f569591e1bfb0e405b8091f99fec45d3_***_Fatemeh Torabi
author Fatemeh Torabi
author2 Guangquan Li
Hubert Denise
Peter Diggle
Jasmine Grimsley
Chris Holmes
Daniel James
Radka Jersakova
Callum Mole
George Nicholson
Camila Rangel Smith
Sylvia Richardson
William Rowe
Barry Rowlingson
Fatemeh Torabi
Matthew J. Wade
Marta Blangiardo
format Journal article
container_title Environment International
container_volume 172
container_start_page 107765
publishDate 2023
institution Swansea University
issn 0160-4120
doi_str_mv 10.1016/j.envint.2023.107765
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
active_str 0
description The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks.We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation.We evaluate the model’s predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks).The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.
published_date 2023-02-01T05:31:35Z
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