RESUMO
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 models 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.
RESUMO
Aimsto investigate the spatiotemporal distribution of COVID-19 cases in England; to provide spatial quantification of risk at a high resolution; to provide information for prospective antigen and serological testing. ApproachWe fit a spatiotemporal Negative Binomial generalised linear model to Public Health England SARS-CoV-2 testing data at the Lower Tier Local Authority region level. We assume an order-1 autoregressive model for case progression within regions, coupling discrete spatial units via observed commuting data and time-varying measures of traffic flow. We fit the model via maximum likelihood estimation in order to calculate region-specific risk of ongoing transmission, as well as measuring regional uncertainty in incidence. ResultsWe detect marked heterogeneity across England in COVID-19 incidence, not only in raw estimated incidence, but in the characteristics of within-region and between-region dynamics of PHE testing data. There is evidence for a spatially diverse set of regions having a higher daily increase of cases than others, having accounted for current case numbers, population size, and human mobility. Uncertainty in model estimates is generally greater in rural regions. ConclusionsA wide range of spatial heterogeneity in COVID-19 epidemic distribution and infection rate exists in England currently. Future work should incorporate fine-scaled demographic and health covariates, with continued improvement in spatially-detailed case reporting data. The method described here may be used to measure heterogeneity in real-time as behavioural and social interventions are relaxed, serving to identify "hotspots" of resurgent cases occurring in diverse areas of the country, and triggering locally-intensive surveillance and interventions as needed. CaveatsThere is general concern over the ability of PHE testing data to capture the true prevalence of infection within the population, though this approach is designed to provide measures of spatial prevalence based on testing that can be used to guide further future testing effort. Now-casts of epidemic characteristics are presented based on testing data alone (as opposed to "true" prevalence in any one area). The model used in this analysis is phenomenological for ease and speed of principled parameter inference; we choose the model which best fits the current spatial case timeseries, without attempting to enforce "SIR"-type epidemic dynamics.
