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A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
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
,
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
Environment International, Volume: 172, Start page: 107765
Swansea University Author:
Fatemeh Torabi
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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...
| Published in: | Environment International |
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| ISSN: | 0160-4120 |
| Published: |
Elsevier BV
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71654 |
| 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 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. |
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| Keywords: |
SARS-CoV-2; Wastewater viral concentration; Bayesian spatio-temporal model; Spatial prediction; Probabilistic detection |
| College: |
Faculty of Medicine, Health and Life Sciences |
| 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’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. |
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107765 |