During the COVID-19 pandemic, the potential use of wastewater-based epidemiology as an early warning tool has been explored widely across the globe. To date, much of the existing work has focused on modelling the SARS-CoV-2 RNA concentration in wastewater at the level of sewage treatment work, where wastewater samples were obtained and concentration of viral contents was measured. There has been no attempt to predict wastewater viral concentration at a fine spatio-temporal resolution over an entire country, a necessary step to use wastewater data for early detection of local outbreaks. In the first part of this talk, I will describe how we predict weekly wastewater viral concentration for the whole of England at fine spatial scales using a geostatistical model. The second part of the talk will focus on a data integration framework that combines the space-time wastewater predictions with the prevalence estimates obtained from public health data collected through randomised surveys and diagnostic testing. Through this framework, I will (a) demonstrate the vital role that wastewater data can play in the “post-COVID” era where collection of public health data on COVID operates at a reduced capacity and (b) discuss some implications on data collection for effective surveillance of the disease.