Abstract During the COVID-19 pandemic, the potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive modelling 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, there is no model to predict viral concentration in wastewater at fine spatio-temporal resolutions over an entire country, a necessary step to realise the potentials of using wastewater data to support the detection of local outbreaks. In this talk, we introduce a spatially continuous geostatistical model that quantifies the relationship between weekly viral concentrations and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at the STW catchment area level while accounting for spatial and temporal correlation. We use the wastewater measurements obtained at the 303 STWs in England over the period between 1 June 2021 and 30 March 2022 for model fitting. We predict the weekly viral concentration over the 32,844 lower super output areas (LSOAs) in England. We can aggregate these LSOA prediction to any geography that is relevant to public health policy-making. In addition to providing weekly maps of viral concentration at fine spatial resolutions for the whole of England, we can use the model outputs to quantify the probability of local changes in wastewater viral concentration over short time periods, which can be used as an early warning tool for public health surveillance.