TY - JOUR
T1 - A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
AU - Li, Guangquan
AU - Denise, Hubert
AU - Diggle, Peter
AU - Grimsley, Jasmine
AU - Holmes, Chris
AU - James, Daniel
AU - Jersakova, Radka
AU - Mole, Callum
AU - Nicholson, George
AU - Smith, Camila Rangel
AU - Richardson, Sylvia
AU - Rowe, William
AU - Rowlingson, Barry
AU - Torabi, Fatemeh
AU - Wade, Matthew J.
AU - Blangiardo, Marta
N1 - Funding information: 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.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - 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.
AB - 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.
KW - SARS-CoV-2
KW - Wastewater viral concentration
KW - Bayesian spatio-temporal model
KW - Spatial prediction
KW - Probabilistic detection
UR - http://www.scopus.com/inward/record.url?scp=85147199796&partnerID=8YFLogxK
U2 - 10.1016/j.envint.2023.107765
DO - 10.1016/j.envint.2023.107765
M3 - Article
C2 - 36709674
SN - 0160-4120
VL - 172
JO - Environment international
JF - Environment international
M1 - 107765
ER -