Wastewater monitoring will be woven into a single statistical framework alongside GP visits, hospital admissions, and 111 calls to track disease outbreaks in real time. During the COVID-19 pandemic, the UK relied on multiple separate surveillance systems—clinical tests, symptom-tracking apps, and wastewater sampling—but no validated method existed to combine them into a coherent picture. Each source has its own biases: wastewater measurements vary with population density and virus shedding rates, while clinical data miss asymptomatic cases. This project will build a modular statistical model that accounts for those biases and links each data source probabilistically, so that all available information contributes to estimating the true number of new cases. If successful, the framework will give public health officials a single, reliable estimate of disease activity for multiple pathogens—starting with COVID-19 and norovirus—and trigger targeted responses such as deploying hospital beds or launching local interventions. The system is designed to be extended to any pathogen that becomes a public health threat, turning a patchwork of noisy data streams into a coherent early-warning system.
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Epidemiological surveillance is of crucial importance to monitor a population's health and to efficiently prioritise healthcare resources. Surveillance methods need to deliver unbiased estimates of local health metrics (in space and time) and to detect meaningful departures from expectation that can trigger consideration of a focal public health response. The COVID-19 pandemic has highlighted the importance of combining community surveillance with traditional diagnostic health data, at the same time flagging the need for a validated method to integrate these sources. This project will build a public health surveillance framework that employs advanced statistical methods to synthesise multiple data sources. It will make use of the ever-increasing healthcare data available in the UK, collected through administrative registries (e.g. hospital admissions and deaths), randomised surveys, as well as through syndromic sources such as GP prescriptions and visits, 111 calls, symptoms apps. Additionally, wastewater monitoring was extensively used as an economically efficient method to monitor COVID-19 circulating in communities and has the potential of being a key component in an integrated surveillance system. However, the concentration of contaminants in wastewater can be affected by population characteristics that vary in space and time, as well as by changes related to the shedding of the viruses. Consequently, while some studies have established an association between aggregated wastewater and clinical measurements (e.g., lateral flow tests), this relationship has been shown to vary over space and time, to be non-linear and likely disease-specific. We will build a modular framework where each data source will be modelled within a module to account for uncertainties and potential biases. This collection of data modules will then be linked probabilistically so that all available data will contribute to the estimation of the underlying disease process. This in turn will provide vital information (for instance number of new cases) to inform where and when additional sources need to be swiftly deployed to reduce the burden of one or more diseases on the health system and on the population (e.g. how many hospital beds are needed or if specific interventions need to be put in place to reduce the disease burden). We will pay particular attention to the modelling and the utilisation of wastewater data within our multiplex system to inform the debate about the added value of using environmental surveillance in combination with traditional epidemiological metrics to form new indicators to answer surveillance questions. We will focus on disease-specific case studies (e.g. COVID, norovirus) to test and optimise the proposed surveillance framework but our ambition is to extend and operationalise the proposed framework to monitor an evolving suite of pathogens/diseases that might be at risk of becoming a public health threat.
Alilsha Davies (Co-Investigator)Christopher Williams (Co-Investigator)David Jones (Co-Investigator)Guangquan Li (Co-Investigator)Marta Blangiardo (Principal Investigator)Peter Diggle (Co-Investigator)
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