Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023089429 |
| _version_ | 1797429775452602368 |
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| author | Fatemeh Torabi Guangquan Li Callum Mole George Nicholson Barry Rowlingson Camila Rangel Smith Radka Jersakova Peter J. Diggle Marta Blangiardo |
| author_facet | Fatemeh Torabi Guangquan Li Callum Mole George Nicholson Barry Rowlingson Camila Rangel Smith Radka Jersakova Peter J. Diggle Marta Blangiardo |
| author_sort | Fatemeh Torabi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T09:19:04Z |
| format | Article |
| id | doaj.art-9bb6cb9eaa3b4787b60160e4ad6c5a58 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-09T09:19:04Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-9bb6cb9eaa3b4787b60160e4ad6c5a582023-12-02T07:04:02ZengElsevierHeliyon2405-84402023-11-01911e21734Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal modelsFatemeh Torabi0Guangquan Li1Callum Mole2George Nicholson3Barry Rowlingson4Camila Rangel Smith5Radka Jersakova6Peter J. Diggle7Marta Blangiardo8Turing-RSS Health Data Lab, London, UK; Population Data Science HDRUK-Wales, Medical School, Swansea University, Wales, UKTuring-RSS Health Data Lab, London, UK; Applied Statistics Research Group, Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UKTuring-RSS Health Data Lab, London, UK; The Alan Turing Institute, London, UKTuring-RSS Health Data Lab, London, UK; University of Oxford, Oxford, UKTuring-RSS Health Data Lab, London, UK; CHICAS, Lancaster Medical School, Lancaster University, England, UKTuring-RSS Health Data Lab, London, UK; The Alan Turing Institute, London, UKTuring-RSS Health Data Lab, London, UK; The Alan Turing Institute, London, UKTuring-RSS Health Data Lab, London, UK; CHICAS, Lancaster Medical School, Lancaster University, England, UK; Corresponding author. Turing-RSS Health Data Lab, London, UK.Turing-RSS Health Data Lab, London, UK; MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College, London, UK; Corresponding author. Turing-RSS Health Data Lab, London, UK.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.http://www.sciencedirect.com/science/article/pii/S2405844023089429Wastewater-based surveillanceWastewater-based epidemiologyCOVID-19Spatio-temporal statistical modelling |
| spellingShingle | Fatemeh Torabi Guangquan Li Callum Mole George Nicholson Barry Rowlingson Camila Rangel Smith Radka Jersakova Peter J. Diggle Marta Blangiardo Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models Heliyon Wastewater-based surveillance Wastewater-based epidemiology COVID-19 Spatio-temporal statistical modelling |
| title | 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_short | 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 |
| topic | Wastewater-based surveillance Wastewater-based epidemiology COVID-19 Spatio-temporal statistical modelling |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023089429 |
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