Background: Simulation models are computational tools that use detailed logic, data, and computer code to provide a quantitative way for researchers to make predictions about drug effectiveness, and health services operational flow. These models are used extensively in health and medical research to assess the effects of changes to patient care and to manage and understand pandemics like Covid-19. The most common approach used in these studies is called discrete-event simulation. Challenges: Very few published studies using discrete-event simulation meet the scientific standard for being open to (re)use, and scrutiny by others. In contrast, fields outside of healthcare, such as Ecology, have seen growth in model sharing. This means that healthcare results are more difficult to fully check or reproduce, and models are not tested for mistakes. Even when a model is shared with a scientific paper, there are considerable challenges in installing specialist simulation software, researcher concerns about intellectual property, time/effort needed to do the sharing, and how long a model remains available.

Software is fundamental to research, fulfilling many different roles - instrument, model, tool, infrastructure - across all disciplines. Recent shifts in the wider research landscape, e.g. inclusion of research software in policies developed by the OECD and UNESCO, necessitate new approaches to software sustainability and consolidation and scaling of existing initiatives to support research software (the software used in research) and digital research infrastructure (the compute, data, networking, software and people infrastructure) that enables it. Thus far, support for research software has tended to focus on individuals or national policies and standards. Moving forward, organisations such as universities and other research institutions will play an increasingly important role in ensuring research software culture and practice is adopted by the research community. This is essential to empower those engaged in research to fully harness the potential of software and foster the execution of excellent research. The Software Sustainability Institute (SSI) was established in 2010 as the first organisation in the world dedicated to the development and support of research software best practices. In its first phase (2010 - 2015), the SSI gained an understanding of the state of the nation of research software, its developers/users, its requirements, and the importance of software for conducting research. The second phase (2015 - 2019) focussed on supporting communities to become self-sustaining and campaigning for change in research culture. In the third phase (2018 - 2024), the SSI consolidated its position as world-leading experts in research software policy and best practices. The SSI also scaled up its highly successful activities to make them more sustainable in the longer term. Throughout, the SSI has fostered a large, collaborative, worldwide community of advocates and practitioners to help deliver on their motto: better software, better research. The fourth phase of the SSI will continue enhancing and scaling its signature activities, including the fellowship programme, community building, career development and training. It will continue to campaign for the recognition of all of the people and outputs that contribute to research and add a new focus on environmental sustainability and empowering organisations to take responsibility for the research software they create and use. Four impacts will guide the work in SSI-4: 1. Evidence-driven research software policy and guidance. 2. Capable research communities. 3. Widespread adoption of research software best practice. 4. Broadened access and contributions to the research software community. The SSI will achieve these through: - Building on its successful platforms and campaigns: empowering individuals through the Fellowship Programme, amplifying dissemination through online resources and social media, raising awareness of research software through events, and campaigning for policy and research culture change. - Growing its policy and research activities: building on SSI national landscape studies, collaborating on the HiddenREF campaign, creating new connections to further embed software into UK research policy. - Developing new training courses, learning pathways, communities of practice handbooks, and bringing the community together through the Collaborations Workshop. - Co-producing research to explore the barriers and enablers to career progress, commissioning articles and guides from a diverse range of authors, and running workshops in other, non-English, languages. - Coordinating an innovative software funding pilot to better understand how research software maintenance and development should be funded.

Networks of Cardiovascular Digital Twins (CVD-Net): Transforming Healthcare through Personalised Predictive Modelling The Networks of Cardiovascular Digital Twins (CVD-Net) Programme Grant aims to revolutionise healthcare by harnessing the power of digital twin (DT) technology. Patient DTs are virtual replicas that continuously assimilate patient data into sophisticated models to provide personalised predictions and inform clinical decisions. Healthcare, despite its national importance (consuming 12% of GDP, generating £70 billion/year and 240,000 jobs), remains unserved by DT technologies. CVD-Net will build a critical mass of research around patient DTs for healthcare, identify the challenges and opportunities in the clinical setting, and provide a roadmap for NHS implementation. We take the view that we must begin by focussing on a specific clinical use case, and that we need to learn by doing, using real-world data, on clinical timescales and making testable predictions. We propose a flexible Programme structure built around developing a minimum viable DT, then testing, optimising, and evaluating the this over iterative design cycles. We focus on pulmonary arterial hypertension (PAH), a life-threatening cardiovascular disease with high mortality and adverse event rates, as a specific use case to develop a demonstrator NHS DT care pathway. The public and patients are receptive to the idea of DTs with 90% (173/196) agreeing with the statement "I would find a digital twin smartphone app that represents my individual cardiovascular health useful". PAH patients suffer high mortality, frequent clinical worsening events and are served by a limited number of national centres. These high event rates and concentration of patients make it possible (and important) to develop and test the forecasting capabilities of a DT in proof-of-concept studies within CVD-Net. Our objective is to create a comprehensive patient DT that can monitor and forecast disease progression, treatment response, and quality of life for individual patients. The DTs will combine data from hospitals, wearable and implantable sensors, and patient-reported outcomes. To realise DTs at the scale and speed of a clinical service, we propose a novel networking approach, where individual "digital threads" (within a DT) will be 'woven' together to form an interconnected 'digital tapestry' to facilitate shared learning and communication. We will utilise innovative techniques including knowledge graphs, transfer learning, federated learning, and meta-learning to address scalability, variability, uncertainty, and data security challenges. We have brought together a unique interdisciplinary team of engineers, clinicians, computational statisticians, and research engineers to deliver CVD-Net. We will access retrospective and collect prospective data to train, test and validate the network of DTs. We will build the IT infrastructure, and analysis workflows to run a demonstrator DT care pathway within the NHS infrastructure. We will work with patients, clinicians, and stakeholders to assess its usability and added value. Via stakeholder engagement, we will evaluate the feasibility, scalability, and wider adoption potential of networked patient DTs in patient care. By generating robust evidence and understanding patient, clinician, and policy considerations, by completion of CVD-Net, we aim to have moved DTs towards prospective evaluation in a clinical trial. Ultimately, CVD-Net has the potential to transform healthcare by providing personalised predictive modelling, enhancing clinical decision-making, and improving patient outcomes. Its applications will benefit patients, clinicians, policymakers, and the research community, making healthcare more precise and efficient while contributing to the transformation of NHS care.