Brazil, like many other low and middle-income countries (LMICs), is undergoing a cancer transition whereby cancers with an infection-related aetiology are being surpassed by those with a non-communicable origin. This transition is particularly evident among women, with breast cancer (BC) having now surpassed cervical cancer (CC) as the most common female cancer in Brazil (estimated number of incident cases in 2012: ~67,000 BC vs. ~18,000 CC). The Sistema Único de Saude (SUS) was established by the Brazilian government in 1988 to provide universal free access to health care. In 1999-2002 guidelines were issued for women aged 25-59 years to undergo a gynaecological examination every 3 years, and in 2004 for mammographic screening every two years of those aged 50-64, with information systems established to manage these screening activities. SISCOLO, implemented in 1999, records information on all Pap smear requests whereas SISMAMA, established in 2009, records data on all mammograms. Despite the success of these programs, and the lower BC incidence in Brazil than in the UK (59.5/100,000 vs. 95/100,000 in 2012), BC mortality is similar (14.3/100,000 vs. 17.1/100,000, respectively) and both CC incidence and mortality remain much higher in Brazil (16.3/100,000 and 7.3/100,000, respectively) than in the UK (7.1/100,000 and 1.8/100,000, respectively). Such disproportionately higher mortality rates are likely to be related to low coverage of the population targeted by screening and late presentation. It is the aim of this Research Partnership to investigate whether this is the case, in the first instance, by creating the tools necessary to study access to early diagnosis and treatment for the two most common female cancers - BC and CC. In particular we will: 1. Gain access to, and link, at a national level, SISMAMA and SISCOLO national data to Censuses, hospital and mortality records, socioeconomic surveys, and Bolsa Familia. 2. Develop a multi-disciplinary conceptual framework to identify demand (e.g. socio-economic status, belief system) and supply determinants (e.g. services availability) of inequalities in health care using such comprehensive data linkage. 3. Strengthen analytic capacity to deal with the methodological challenges raised by interrogating large routinely-collected datasets. These include addressing potential sources of bias affecting the linked data and the adoption of appropriate statistical methodology for examining pathways to inequalities in health care.

Computational biomedicine offers many avenues for taking full advantage of emerging exascale computing resources and, as such, will provide a wealth of benefits as a use-case within the wider ExCALIBUR initiative. These benefits will be realised not just via the medical problems we elucidate but also through the technical developments we implement to enhance the underlying algorithmic performance and workflows supporting their deployment. Without the technical capacity to effectively utilise resources at such unprecedented scale - either in large monolithic simulations spread over the equivalent of many hundreds of thousands of cores, in coupled code settings, or being launched as massive sets of tasks to enhance drug discovery or probe a human population - the advances in hardware performance and scale cannot be fully capitalised on. Our project will seek to identify solutions to these challenges and communicate them throughout the ExCALIBUR community, bringing the field of computational biomedicine and its community of practitioners to join those disciplines that make regular use of high-performance computing and are also seeking to reach the exascale. In this project, we will be deploying applications in three key areas of computational biomedicine: molecular medicine, vascular modelling and cardiac simulation. This scope and diversity of our use cases mean that we shall appeal strongly to the biomedical community at large. We shall demonstrate how to develop and deploy applications on emerging exascale machines to achieve increasingly high-fidelity descriptions of the human body in health and disease. In the field of molecular modelling, we shall develop and deploy complex workflows built from a combination of machine learning and physics-based methods to accelerate the preclinical drug discovery pipeline and for personalised drug treatment. These methods will enable us to develop highly selective small molecule therapeutics for cell surface receptors that mediate key physiological responses. Our vascular studies will utilise a combination of 1D, 3D models and machine learning to examine blood flow through complex, personalised arterial and venous structures. We will seek to utilise these in the identification of risk factors in clinical applications such as aneurysm rupture and for the management of ischaemic stroke. Within the cardiac simulation domain, a new GPU accelerated code will be utilised to perform multiscale cardiac electrophysiology simulations. By running large populations based on large clinical datasets such as UK Biobank, we can identify individual at elevated risk of various forms of heart disease. Coupling heart models to simulations of vascular blood flow will allow us to assess how problems which arise in one part of the body (such as the heart) can cause pathologies on remote regions. This exchange of knowledge will form a key component of CompBioMedX. Through this focussed effort, we will engage with the broader ExCALIBUR initiative to ensure that we take advantage of the efforts already underway within the community and in return reciprocate through the advances made with our use case. Many biomedical experts remain unfamiliar with high-performance computing and need to be better informed of its advantages and capabilities. We shall engage pro-actively with medical students early in their career to illustrate the benefits of using modelling and supercomputers and encourage them to exploit them in their own medical research. We shall engage in a similar manner with undergraduate biosciences students to establish a culture and practice of using computational methods to inform the experimental work underpinning the basic science that is the first step in the translational pathway from bench to bedside.