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
Author Information
Guangquan Li: Applied Statistics Research Group, Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK; Turing-RSS Health Data Lab, UK.
Hubert Denise: Analytics & Data Science Directorate, UK Health Security Agency, Nobel House, Smith Square, London SW1P 3JR, UK.
Peter Diggle: Lancaster University, Lancaster LA1 4YW, UK; Turing-RSS Health Data Lab, UK.
Jasmine Grimsley: Analytics & Data Science Directorate, UK Health Security Agency, Nobel House, Smith Square, London SW1P 3JR, UK.
Chris Holmes: University of Oxford, Oxford, UK; The Alan Turing Institute, London NW1 2DB, UK; Turing-RSS Health Data Lab, UK.
Daniel James: Analytics & Data Science Directorate, UK Health Security Agency, Nobel House, Smith Square, London SW1P 3JR, UK.
Radka Jersakova: The Alan Turing Institute, London NW1 2DB, UK; Turing-RSS Health Data Lab, UK.
Callum Mole: The Alan Turing Institute, London NW1 2DB, UK; Turing-RSS Health Data Lab, UK.
George Nicholson: University of Oxford, Oxford, UK; Turing-RSS Health Data Lab, UK.
Camila Rangel Smith: The Alan Turing Institute, London NW1 2DB, UK; Turing-RSS Health Data Lab, UK.
Sylvia Richardson: MRC Biostatistics Unit, East Forvie Site, Cambridge CB20SR, UK; Turing-RSS Health Data Lab, UK.
William Rowe: Analytics & Data Science Directorate, UK Health Security Agency, Nobel House, Smith Square, London SW1P 3JR, UK.
Barry Rowlingson: Lancaster University, Lancaster LA1 4YW, UK; Turing-RSS Health Data Lab, UK.
Fatemeh Torabi: Swansea University Medical School, Faculty of Medicine, Health Life Science, Swansea SA2 8PP, UK; Turing-RSS Health Data Lab, UK.
Matthew J Wade: Analytics & Data Science Directorate, UK Health Security Agency, Nobel House, Smith Square, London SW1P 3JR, UK.
Marta Blangiardo: MRC Centre for Environment and Health, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK; Turing-RSS Health Data Lab, UK.
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.
