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Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models

Fatemeh Torabi Orcid Logo, Guangquan Li Orcid Logo, Callum Mole Orcid Logo, George Nicholson Orcid Logo, Barry Rowlingson Orcid Logo, Camila Rangel Smith, Radka Jersakova, Peter J. Diggle, Marta Blangiardo

Heliyon, Volume: 9, Issue: 11, Start page: e21734

Swansea University Author: Fatemeh Torabi Orcid Logo

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DOI (Published version): 10.1016/j.heliyon.2023.e21734

Abstract

The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use o...

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Published in: Heliyon
ISSN: 2405-8440
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa71652
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Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). 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spelling 2026-04-20T14:04:47.2513174 v2 71652 2026-03-19 Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models f569591e1bfb0e405b8091f99fec45d3 0000-0002-5853-4625 Fatemeh Torabi Fatemeh Torabi true false 2026-03-19 MEDS The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised. Journal Article Heliyon 9 11 e21734 Elsevier BV 2405-8440 Wastewater-based surveillance; Wastewater-based epidemiology; COVID-19; Spatio-temporal statistical modelling 1 11 2023 2023-11-01 10.1016/j.heliyon.2023.e21734 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Another institution paid the OA fee This work was funded by The Department for Health and Social Care (Grant ref: 2020/045) with additional support from The Alan Turing Institute (EP/W037211/1) and in-kind support from The Royal Statistical Society. 2026-04-20T14:04:47.2513174 2026-03-19T23:23:08.3846987 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Fatemeh Torabi 0000-0002-5853-4625 1 Guangquan Li 0000-0002-8736-5349 2 Callum Mole 0000-0002-1463-6419 3 George Nicholson 0000-0001-9588-6075 4 Barry Rowlingson 0000-0002-8586-6625 5 Camila Rangel Smith 6 Radka Jersakova 7 Peter J. Diggle 8 Marta Blangiardo 9 71652__36535__f231296a41104523b1ee4228f4331417.pdf 71652.VoR.pdf 2026-04-20T14:02:54.4797188 Output 1532285 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 Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models
spellingShingle Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models
Fatemeh Torabi
title_short Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models
title_full Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models
title_fullStr Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models
title_full_unstemmed Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models
title_sort Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models
author_id_str_mv f569591e1bfb0e405b8091f99fec45d3
author_id_fullname_str_mv f569591e1bfb0e405b8091f99fec45d3_***_Fatemeh Torabi
author Fatemeh Torabi
author2 Fatemeh Torabi
Guangquan Li
Callum Mole
George Nicholson
Barry Rowlingson
Camila Rangel Smith
Radka Jersakova
Peter J. Diggle
Marta Blangiardo
format Journal article
container_title Heliyon
container_volume 9
container_issue 11
container_start_page e21734
publishDate 2023
institution Swansea University
issn 2405-8440
doi_str_mv 10.1016/j.heliyon.2023.e21734
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
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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
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description The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.
published_date 2023-11-01T05:31:35Z
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