A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

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-2 N1 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 is able to 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.

Strategic use of SARS-CoV-2 wastewater concentration data could enhance, but not replace, high-resolution community prevalence survey programmes

“…We employed the lookup table provided by [28], which gives a mapping of wastewater catchment areas to Lower layer Super Output Areas (LSOAs) which are small regional geographies that combine to form the larger geography of an LTLA. For 21 STWs of the EMHP programme which are not included in the lookup table, to estimate their catchment areas, we employed an approximation method proposed by [13].…”

“…Several studies have established strong correlations between wastewater SARS-CoV-2 concentrations and reported COVID-19 cases [6,7,8], whilst others have estimated the time dependency and lead time (varying from four to ten days) from detection in wastewater to date of testing in clinical cases [9,10,11]. During the pandemic, SARS-CoV-2 prevalence in England was estimated for 45 sewage site catchments [12], whilst further research estimated the weekly viral wastewater concentrations in a spatially continuous domain [13].…”