Antimicrobial resistance is increasing in nature and threatens the effectiveness of our drug therapies and infection control. However, it remains difficult to distinguish what originates from human activities or what is natural. Therefore, we must extend the scale and depth monitoring efforts to better understand what is driving the increases in resistance traits. This project will use two collections of previously characterised soils to compare and contrast distributions of AR genes under widely varying conditions, ranging from urban, agriculture, legacy mining, and pristine rural environments. The project will utilise DNA extractions and new genetic technology to quantify over 230 AR genes in the samples. Soil inventories provide us well-characterised soils and the wealth of information that describes both the soils and the impacts at source locations. The project will generate an astonishing 120,000 AR-related data points (400 locations x 300 genes), each with extended background information on environmental conditions-creating among the largest geographic representation of AR gene distribution across landscapes ever created; sufficiently detailed to make cross-cutting observations of landscape effects on acquired vs innate AR levels. With advanced multi-parametric statistics, we will relate specific environmental conditions and factors with observed AR genes levels in soils to identify risk factors associated resistance development and impacts on human and agricultural health.

Wastewater-Based Epidemiology (WBE) requires relatively few resources compared to the systematic testing of populations. WBE is especially promising for novel infectious diseases, where asymptomatic cases might play a significant role in transmitting the virus. However, WBE is only now being used to monitor the spread of a pandemic infectious disease. Early studies by ourselves and others have shown that SARS-CoV-2 RNA can be recovered from wastewater, including from wastewater treatment plants (WWTP) preceding local COVID-19 hospitalisation activity. Given the challenge of making available diagnostic tests to the entire UK population, WBE represents a potentially low-cost and immediate mechanism for understanding levels of infection within large geographic areas. N-WESP aims to compare our methods with those of European & North American WBE teams in an inter-lab trial for understanding, supporting and improving the DEFRA COVID-19 measurements which will feed into the Joint Biosecurity Centre (JBC). We will also compare methods with DEFRA, the EA's and JBC whilst they explore options for finer geographical measurements. N-WESP will empower public health authorities with an optimised surveillance tool with maximal sensitivity and predictive power whose uncertainties have been well characterised. N-WESP will determine whether SARS-CoV-2 RNA in wastewater and sludge is infectious, and to what extent there might be downstream risks to human health. N-WESP will exploit catchment and, uniquely, sub-catchment-scale longitudinal surveillance to understand temporal and spatial heterogeneity, relationships to human disease burden distribution and whether there is potential outbreak 'hotspots' by surveilling sewer system nodes.

I-MOVE-COVID-19 aims to obtain epidemiological, clinical and virological information about COVID-19 and patients infected with SARS-CoV-2, through provision of a flexible surveillance platform (adaptable to the epidemiological situation), research studies, hypothesis-testing and evaluation of public health interventions (e.g. vaccination, antivirals) in order to contribute to the knowledge base, guide patient management, and inform the public health response. This will be achieved through adaptation and expansion of the existing, long-running, Europe-wide influenza surveillance network (I-MOVE) to include COVID-19. The network includes primary care networks, hospitals, national laboratory reference centres in eleven countries. I-MOVE-COVID-19 priority activities and research projects will be selected based on ECDC/WHO input and the proposal’s detailed list. These will be conducted by mobilising an existing large European multidisciplinary network, combining the expertise and resources of groups working in surveillance (epidemiological, clinical, virological), respiratory disease research, and evaluation of vaccines/treatments. Through protocol sharing and pooling European results, questions will be answered which could not be efficiently answered by countries acting alone. I-MOVE-COVID-19 will share study results rapidly and widely with national and international partners and with the wider scientific community and contribute to clinical management of patients and public health preparedness and response to COVID-19.

The 2019 Climate Change Act committed the UK to reducing its emissions of greenhouse gases to net zero by 2050. The 2019 UK Clean Air Strategy, sees "air pollution as one of the UK's biggest public health challenges", aims to secure clean growth whilst tackling air pollution through reducing emissions. Achieving these reductions in greenhouse gas and air pollutant emissions will entail substantial reductions in use of fossil fuels and changes to the transport fleet over coming years as we make the transition to a 'low carbon economy'. This will also have an important benefit for health of improving levels of outdoor air pollution by reducing emissions from power plants, motor vehicles, wood/coal burning at home and other sources. However, another important climate change action is to improve energy efficiency in homes. Those measures typically entail reducing levels of ventilation to cut down heat losses from escape of heated air. In addition to helping improve winter indoor temperatures, this can be beneficial for human health because it reduces the penetration into the home of air pollutants from the outdoor environment. But it will increase indoor levels of air pollutants derived from sources inside the home - such as particles and gases generated by cooking, volatile organic compounds (VOCs) given off from fabrics and furnishings, cleaning and personal care products. The changes to indoor pollution levels from improved home energy efficiency may thus be overall positive or negative for the health of building occupants depending on the balance of effects on pollutants entering and leaving the indoor environment. That balance is likely to depend on the levels of outdoor pollutants, indoor air pollutant sources and activities that generate these, the form of the energy efficiency improvements, the behaviour of occupants and their vulnerability to air pollutants. People at particular risk are young children, the elderly, those with pre-existing illnesses, and those experiencing social deprivation. To improve understanding of these issues, we have created a new research network (acronym 'HEICCAAM'). This network brings together experienced and early career researchers from nine universities from disciplines as diverse as air quality measurement and modelling, building physics, behavioural science, health and health inequalities, education and policy. The network will also include representatives of the public, as well as stakeholders from the public sector, business/industry and non-government bodies - including Public Health England, Health Protection and NHS Scotland, Scottish Environment Protection Agency, Age UK, the Passivhaus Trust, Good Homes Alliance, Edinburgh City Council, the Chartered Institution of Building Services Engineers and the UK Met Office. The network will build evidence on the consequences for exposure to air pollution of actions aimed at tackling climate change and poor air quality, with particular focus on the home environment. Its aim is to provide underpinning research that can inform and influence policy and practice to safeguard human health. The network will include activities by six Working Groups tasked with generating a series of papers on relevant issues of science and policy. It will also undertake four small research projects aimed at improving understanding of key issues where there are knowledge gaps. It will have a particular focus on protecting the health of vulnerable groups and reduction of health inequities. Network members will have multiple interactions through electronic meetings, webinars, discussion groups and an annual meeting and workshop with a wider group of stakeholders. Through its activities, the network will help build long-term capability in interdisciplinary research in this area, including through the interactions with early career researchers, the development of new research plans, and linkage to other networks and existing research programmes.

The health sciences have seen an explosion in the amount of data collected at both individual and population levels. This data can be varied, including genetic information, health records, data on activity levels obtained from wearable devices, and image data from scans. There is huge potential for improved diagnoses, timely interventions and more effective treatments if we can fully extract understanding from this data. Example applications included real-time monitoring of patients, developing personalised treatment, or real-time monitoring and decision-making for epidemics. However the data science challenges in extracting these insights are vast. Features of these challenges include the need to make inferences about and decisions for individuals from within a population, and the need to synthesise information from disparate data sources and data types. Whilst we have substantial data collected at a population level, the amount of information on any given individual may be still be limited. Appropriately quantifying uncertainty is crucial for making decisions, with the optimal decision often being driven by the probability of relatively rare events (e.g. extreme reaction to a drug). We need model-based approaches to data science that can leverage scientific understanding, but we need the statistical analyses to be robust to unavoidable inadequacies of these models. Underpinning many of these applications is the requirement to develop new understanding, and this differs from a focus on making predictions that it is most common among current statistical or machine learning methods. Bayesian data science provides a natural framework for tackling these challenges. Bayesian methods are model-based, can appropriately quantify and propagate uncertainty, and through hierarchical models are able to use population-level information when making inferences about individuals. Repeated application of Bayes theorem gives a natural paradigm for synthesizing information across multiple data sources. However, current Bayesian data science methods are not feasible for many modern, big-data, applications in the health sciences. Bayesian methods require integrating over uncertainty. Such high-dimensional integration carries a substantial computational overhead when compared to alternative, often optimization-based, data science methods. So while the motivation for Bayesian analysis is clear, this computational overhead means that, currently, implementing Bayesian approaches is often not feasible. This programme of research will develop the new approaches to Bayesian data science that are needed both within the health sciences and more widely. It builds on recent breakthroughs in Monte Carlo integration methods that show great promise for being efficient for large data; and on new paradigms for Bayesian-like updates that are suitable for complex models and which focus modelling effort just on the aspects of these models that are most important. It will address key research challenges in the health sciences -- directly developing new insights and understanding for these.