Without antimicrobial drugs, the risk of bacterial infection would render many common medical procedures too dangerous to contemplate because of the risk of infections caused by "opportunistic bacteria". They can live on the patient's skin, or in their intestines, and infection occurs when bacteria get into parts of the body that are normally sterile. A perfect example is urinary tract infection (UTI) caused by faecal bacteria. E. coli is particularly abundant in human faeces so is perfectly placed to cause opportunistic infections. It is one of the most common causes of healthcare pneumonia, surgical site infection, bloodstream infection and UTI in the UK. In order to prevent against and treat opportunistic infections, patients are given antimicrobials. Almost all antimicrobials are "antibiotics", which means they are derived from natural chemicals produced by microbes found in the environment. Natural antibiotics have been present in the environment for millions of years, and so bacteria living in their presence have had time to evolve mechanisms that can resist their actions, encoded by "resistance genes". Opportunistic bacteria like E. coli can randomly acquire these pre-evolved resistance genes and in a single step, they become insusceptible to a particular antimicrobial. If that insusceptible E. coli colonises a person and then causes an opportunistic infection, the infection will not be treatable with that particular antimicrobial. We refer to this as "antimicrobial resistance" (AMR); however AMR bacteria don't just resist clinical antimicrobial therapy, they beat it. Animals also carry an abundance of E. coli in their intestines and are frequently treated with antimicrobials. This can select for the acquisition of AMR E. coli which can then be passed on to another animals, directly, or via contamination of the environment with faeces. Theoretically, the AMR E. coli could also be passed on to people, and there is much debate about whether such "zoonotic transmission" happens to any significant degree. This is an important debate because it has led to calls from some to dramatically reduce the amount of antimicrobials that are given to animals with the view that it will reduce the level of AMR in animals, and so the possibility of zoonotic transmission to people. But the potential impact on welfare and food production means this should only be done if there is evidence that it will work. In this project we will identify what drives acquisition of AMR in animals using E. coli as the exemplar bacterium and dairy cows and dogs as exemplar farmed and companion animals. We will test whether AMR bacteria encountered by an animal as it interacts with the environment influence the AMR profile in its faeces, and/or whether early life antimicrobial use plays a part in selection of AMR bacteria in animals. We will also test whether reducing antimicrobial use in dairy cows actually does reduce AMR in the near-farm environment that is contaminated with their faeces. We will test whether exercising in these contaminated near-farm environments influences the abundance of AMR bacteria in dogs, and whether there is any evidence of direct acquisition of AMR E. coli by dogs from near-farm environments, which might be brought into the home. Finally, we will investigate whether AMR abundance in human UTI E. coli reduces as antimicrobial drug prescribing reduces in primary care; whether living close to a farm affects AMR abundance in UTI E. coli; whether there is direct evidence for E. coli carried by dogs or found in near-farm environments contaminated by cattle faeces also causing UTIs in humans. These interlaced studies will provide much needed data about the management changes that might reduce AMR in animals and in humans, and are designed to address the fundamental question of whether zoonotic transmission is particularly significant as a driver of AMR in people relative to antimicrobial drug use by doctors.

The widespread use of antibiotics in livestock farming, in many circumstances, increasingly serves as alternative to the diagnosis, targeted treatment and prevention of disease in individual animals, flocks and herds. Relationships and practices between diagnosis, prescription, treatment and prevention have become stretched to the point of rupture, a rupture thrown into sharp focus by the issue of AMR. Better, smarter, more rapid and more accessible diagnoses, driven by a shift in the behaviours and conditions associated with diagnostic decision making (whether performed in the laboratory or at the point-of-care by veterinarians or farmers) represents a critical step to delivering a more effective and sensible use of antibiotic medicines in animal health. Improvements in diagnostic development and practice, however, and in their relationship to prescription and treatment, require social, governance and technical innovations, understanding the parameters and conditions of which demands urgent research. In this proposed research, we ask: "What needs to be in place to develop better conditions for a diagnostic-led approach to animal care and treatment?" This interdisciplinary research team will work with and draw from original, empirically driven information, understanding and analysis from diagnostic tool developers and regulators, veterinary practices and professional bodies, farmers and treatment, decision makers, veterinary laboratories, the food industry and government regulatory authorities to develop durable and innovative strategies for facilitating and advancing smarter approaches to the use of antibiotics in agriculture. We aim to collaboratively generate, evaluate and analyse behaviours and strategies in the practice and governance of animal disease diagnosis, and to show how innovation in the development of diagnostic tools and methods in diagnostic practice along with diagnostic regulation and governance can lead to more sensible use and prescription of antibiotics in animal farming. To do this, we will assess current diagnostic and treatment decision practices in the UK. We will generate understanding of the current development of, market for, and regulation of new and innovative diagnostic tools and technologies. Working with veterinarians, diagnostic developers, farmer and regulators, we will identify pathways and possibilities for improved diagnostic practice and, with partner veterinary practices, will trial new diagnostic tools on a series of farms. We will conduct pilot and capacity-building research in Tanzania, where the relative absence of robust national-level institutions and governance structures for the management of animal disease creates a different context for the coherent stewardship of antibiotic practice and diagnostic use. We will assess the adaptability and responsiveness of the different production sectors (poultry, pigs and cattle), along with a variety of veterinary structures, to the trialled innovations in diagnosis and diagnostics, and will determine the likely benefits of these innovations for prescription practice, for animal health and for livestock production. We will evaluate the implications these innovations will have for the organisation, cost-effectiveness and efficiency of veterinary practice, as well as for veterinary training. We will identify the changes in behaviour, practice and knowledge necessary to accompany the more widespread adoption of novel and innovative practices that are deemed effective. We will assess the regulatory and governance support necessary to see the adoption and use of innovative diagnostic practices. With our project partners, we will develop detailed strategies for the improved use of diagnostic tools and practices to enable more effective and sensible use of antibiotics in livestock agriculture.

In the UK, dairy milk is a key part of the economy and an important source of nutrition. There are several diseases that regularly develop in UK dairy cows which compromise health and welfare, and lead to economic losses for the farmer and industry. Ill cows have also been found to contribute disproportionately to methane emissions and hence the environmental sustainability of the sector. In addition, high welfare is more important than ever to satisfy societal demands for food production. To help farmers detect and treat these diseases, numerous solutions for automated monitoring of dairy cattle are now available to farmers. A critical disadvantage of all these technologies is that they are focussed on detecting the observable symptoms of later stage disease, when treatment options may be limited, reduction of milk production persistent and animal welfare more severely compromised. A cow's response to infection and trauma is to de-prioritise behaviours not immediately essential to survival and recovery - such as social interactions - in favour of those that remain critical for longer, In a recent study we have found that social exploration, the grooming of others and receiving headbutts were lower in individuals with early stage mastitis. We hence hypothesise that social behaviour changes could be early predictors of disease. Detecting social behaviour changes is difficult for the busy farmer, but is possible by monitoring them at key focal points, such as when queueing for milking or feeding at the feed bunk, using video cameras and artificial intelligence (AI). We have developed highly robust AI that can track the motion of cows in video and recognises each individual through their distinctive coat pattern. Others have now demonstrated good classification of affiliative and agonistic social interactions from video and hence we now propose combining the two ideas to track changes in activities and social behaviours over time for each identified cow in a herd. From collecting two years of video from 64 cameras covering the main barn at our John Oldacre Centre dairy farm, we will train a model that learns what types of behaviours change over time that are indicative of different early stage diseases. We will focus on mastitis and lameness, as these diseases have the greatest incidence in our data and are the most important for the UK dairy industry. At the same time, we will sample the saliva of a subset of our herd so we can determine general levels of inflammation, enabling us to see how specific our behavioural predictors are to particular diseases. Dairy farmers are specialists in the behaviour and personalities of their cattle and their input will be vital to helping understand vagaries in farm data and how our system is functioning. We will test our system by deploying it at a network of recruited farms, and will conduct in-depth semi-structured interviews with the farmers regarding their experiences of camera placement (including intrusiveness and social acceptance by farm workers), operation and any other perceived impacts to their farms, farm workers or animal management, health and welfare. It is also critical that we design the system with all facets of industry, to engage their diverse insights and expertise in setting alert levels, designing user-friendly interfaces that will be well placed to be uptaken and discussing additional routes to market such as for disease surveillance. We have therefore assembled a consortium of partners covering all key areas from farmers to vets, the supply chain, data/diagnostic service providers and business development, all of whom we have a proven track record of successful engagement and impact with. Through consultation we will develop a sustainable strategy for meaningful lay stakeholder and public involvement with our system and results, helping to promote a widespread understanding and public/stakeholder acceptance of the system.