Abstract

Wastewater-based epidemiology is valuable surveillance tool that has recently emerged as a cost-effective method for early detection and surveillance of viral outbreaks. Extensive research was conducted during the COVID-19 pandemic. However, there remains a notable gap in the development of spatially explicit models to predict wastewater concentrations of other pathogens, such as norovirus, at fine spatio-temporal resolutions covering entire regions or countries. We consider norovirus, the most common cause of acute gastroenteritis globally. Norovirus surveillance in the UK relies on clinical samples from confirmed outbreaks in hospitals, excluding mild and asymptomatic cases which underestimates the true disease burden. Wastewater-based epidemiology can overcome this issue though being virtually free from selection bias, thereby improving the estimates of norovirus activity. In this study, we address this through specifying a geostatistical model that quantifies the relationship between fortnightly norovirus concentration in sewage treatment works’ (STWs) catchment areas and relevant covariates including indices of deprivation, demographic factors (including proportion of Black, Asian, and Minority Ethnic populations), land use and population mobility. We used data on fortnightly average of flow-normalized norovirus concentration, reported as the number of viral gene copies per 100 000 people, collected from 146 STWs between 27-5-2021 and 30-3-2022. We accounted for spatial and temporal correlations to map fortnightly norovirus concentrations at desired levels of spatial resolution. We then extended the model to predict norovirus activity using public health surveillance data, which is important for policy makers.