Characterising the genetic code ("genome") of an organism can inform on its ability to survive, tolerate drugs and treatments, and its likely geographical source. Researchers can investigate the genome of an organism, and its important mutations (genome "spelling mistakes"), through applying sequencing technologies to its DNA. Cost-effective and rapid sequencing technologies are now being rolled-out in hospitals and clinics to identify important mutations, and thereby prevent disease, diagnose, and personalise treatment of patients. Genome sequencing has become an important diagnostic tool in infectious disease settings, including to identify microorganisms causing infections ("pathogens") and their resistance to drugs, and to track outbreaks. Such knowledge is revolutionizing clinical decision making, public health surveillance and infection control; as demonstrated during the COVID-19 pandemic, where rapid sequencing of the causal SARS-CoV-2 viral genomes has assisted the detection of clinically important mutations (e.g., omicron variants) and informed on their geographical spread ("transmission patterns"). To assist the analysis of the large datasets arising from the sequencing of pathogens, it is important to identify key mutations linked to (severe) patient outcomes, drug resistance, likely geographical source, and other important "barcoding" information that can provide a "profile" of the pathogen underlying any infection. Computer software tools have been developed (e.g., our TB-Profiler and Malaria-Profiler software) that can rapidly analyse sequence data to provide such pathogen profiles, for easy interpretation by medical doctors and infection control specialists. With the increasing use of sequencing technologies in hospitals and clinics, there is a need for Artificial Intelligence (AI) computational methods to analyse the resulting "big data" in real time, including to update the lists of barcoding genetic mutations and to identify if the pathogen genome is related to those previously sequenced i.e., it is being transmitted. We have previously applied AI methods to identify known and novel genetic mutations linked to drug resistance and transmission, as well as created computing repositories (e.g., TB-ML) where the underlying software can be stored, allowing comparisons between statistical models and AI approaches. Our proposed project will integrate these AI-based tools into our profiling software to reveal drug resistance mutation and transmission patterns, and generate informative reports for clinical and infection control decision making. Working within established collaborations involving The UK Health Security Agency and Health ministries in Asia (Bangladesh, Philippines, Thailand, Vietnam), which are routinely using sequencing technologies to inform clinical diagnosis, we will attempt to implement the resulting AI systems software in the UK and overseas settings endemic for infectious diseases. We will initially focus on three main infectious diseases of high global burden, tuberculosis, malaria and Klebsiella infections, with the potential to extend the work to other infections. All sequence data and software developed will be made publicly accessible, leading to their use by other biomedical researchers and healthcare stakeholders. Ultimately, the implementation of such AI-based tools will reduce the burden of infectious diseases, leading to healthier populations and associated economic benefits.

Rabies, a horrific but preventable disease, kills over 200 people annually in the Philippines. The National Rabies Prevention and Control Program in the Philippines has catalysed rabies control efforts with some provinces now aiming to declare freedom from disease. However, incursions and outbreaks continue and human deaths still occur. While improved postexposure prophylaxis (PEP) access has reduced mortality, it has proven expensive. Indeed rising PEP use has put a strain on local and national budgets, even as rabies circulation has declined, raising the question of how these efforts can be sustained. Meanwhile, routine rabies surveillance in the Philippines has major shortcomings and is not sufficiently sensitive for international agencies to recognize rabies free areas or to rapidly respond to incursions which remain a risk while rabies circulates in other provinces. As a result, surveillance measures need strengthening and use of PEP needs rationalizing for the Philippines to fully benefit from rabies control measures that are currently underway. Our overarching aim is to deliver a cost-effective, epidemiologically robust surveillance package that can be rolled out across the Philippines to guide and sustain the elimination of canine rabies. Through implementation research we will develop best practice for an enhanced surveillance approach using Integrated Bite Case Management (IBCM) as a strategy to detect rabid animals, with risk assessment of bite patients triggering epidemiological investigations. IBCM has been identified as a potential strategy that can sufficiently enhance surveillance to enable verification of rabies freedom by international organizations and rapid detection of incursions for effective outbreak responses to maintain rabies freedom. Operationalizing IBCM as a key component of enhanced surveillance will have immediately beneficial applications within the Philippines and is of critical importance for the global campaign to eliminate human rabies deaths by 2030. IBCM has also been demonstrated as an effective way to improve PEP administration, ensuring at risk persons are treated and unnecessary PEP use is reduced. Within our implementation study, we will conduct a pragmatic stepped-wedge cluster randomized controlled trial to evaluate the potential for cost savings and improved patient care through rationalized PEP. We estimate that if implemented effectively, rationalized PEP could save over $9 million every year in the Philippines. Focusing on the low socio-economic class provinces of Romblon, Occidental Mindoro, and Oriental Mindoro, that include geographically isolated and disadvantaged communities, SPEEDIER will provide learning opportunities to local health and veterinary professionals and support communities to attain disease freedom, contributing to the Philippines developmental goals (2014 Kalusugang Pangkalahatan ('Universal Health') Road Map). Integrated, intersectoral, surveillance and response systems are advocated by international agencies, but rarely operationalized in resource-poor settings. Using our detailed epidemiological understanding of rabies and experience of deploying new technologies, we will develop an integrated surveillance and response system that enables effective working between sectors at multiple scales of governance. This is important; international agencies like the World Health Organisation and the World Organisation for Animal Health recognize the essential need to develop effective surveillance and sustainable approaches to guide rabies elimination programmes. The tools and best practice produced by SPEEDIER will therefore be invaluable for the global target to achieve zero human rabies deaths by 2030.