Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom National COVID-19 Surveillance Programmes

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. Early in the COVID-19 epidemic scientists and engineers demonstrated the use of wastewater as a medium by which the virus could be monitored both temporally and spatially. In the United Kingdom this evidence prompted the development of National wastewater surveillance programmes involving UK Government agencies academics and private companies. In terms of speed and scale the programmes have proven to be unique in its efforts to deliver measures of virus dynamics across a large proportion of the populations in all four regions of the country. This success has demonstrated that wastewater-based epidemiology (WBE) can be a critical component in public health protection at regional and national levels and looking beyond COVID-19 is likely to be a core tool in monitoring and informing on a range of biological and chemical markers of human health; some established (e.g. pharmaceutical usage) and some emerging (e.g. metabolites of stress). We present here a discussion of uncertainty and variation associated with surveillance of wastewater focusing on lessons-learned from the UK programmes monitoring COVID-19 but addressing the areas that can broadly be applied to WBE more generally. Through discussion and the use of case studies we highlight that sources of uncertainty and variability that can impact measurement quality and importantly interpretation of data for public health decision-making are varied and complex. While some factors remain poorly understood and require dedicated research we present approaches taken by the UK programmes to manage and mitigate the more tractable components. This work provides a platform to integrate uncertainty management through data analysis quality assurance and modelling into the inevitable expansion of WBE activities as part of One Health initiatives.