This project will deliver computational models of the lung, to support the development of patient-specific treatment strategies for the COVID-19 pandemic. The models will i) automate analysis of the damaged lung, providing additional quantitative data to support more reliable and rapid conclusions about the presentation of the virus, ii) provide predictions of how the lung will perform in response to different management strategies (supplemental oxygen, mechanical ventilation, fluid balance) and potential future treatment strategies outlined in the RECOVERY/REMAP-CAP trial (e.g. steroids, anti-inflammatories, antibiotics and plasma from recovered patients); innovatively factoring specific parameters such as weight, height, age, general fitness and ethnicity - which unquestionably have acute relevance for recovery. COVID-19 is heterogenous - affecting everyone differently. Therefore, rapid and appropriate medical responses to individual cases are critical. Presently patients can remain on ineffective treatment pathways for 4-6 hours before alternative treatment strategies are employed. This project reduces waiting times, enabling prioritisation based on quantitative tools. The models deliver heightened understanding of individual lung mechanics, enabling clinicians to quickly make better informed treatment decisions to optimise COVID-19 survival rates. The model will use patient CT data, patient-specific calibration factors (age, sex, size) and risk factors (comorbidities, clinical frailty score, exercise tolerance, APACHE-II, ethnicity), state-of-the-art image analysis and computer simulation, in collaboration with 3DLifePrints to build human lung models. Patient data will be accessed via ICNARC and the SAIL databank. The model will mimic lung structure and mechanical function, accounting for the effect of tissue damage and providing dynamic feedback of lung health.

DATA-CENTRIC will fundamentally transform modern computational engineering through the development of algorithms that are accountable. This means algorithms capable of quantifying the uncertainty arising from computation itself, delivering simulations that are more transparent, traceable and at the same time more efficient. Crucial decisions in science, engineering, healthcare and public policy rely on established methodologies such as the Finite Element Method and the Stochastic Finite Element Method. However, the models that inform such decisions suffer from an inevitable loss of accuracy due to, and not limited to the following sources of uncertainty: a) time and cost constraints of running modern high-fidelity computer models, b) simplifying approximations necessary to translate mathematical models into computational models, and c) limited numerical precision inherent to any computer system. Therefore, there is a continuous risk of relying on unverified computational evidence, and the path from modelling to decision-making can be (inadvertently or unwillingly) obscured by the lack of accountability. DATA-CENTIC will solve this problem through Probabilistic Numerics, a framework that will enable decision-makers to monitor, diagnose and control the quality of computer simulations. Probabilistic Numerics treats computation as a statistical problem, thus enriching computation with a probabilistic measure of numerical error. This idea is gathering momentum, especially in the UK. However, theoretical development are still in their early stages and except for a few examples, it has not been applied to solve large-scale industrial problems. Consequently, it has not yet been adopted by industry. DATA-CENTRIC will bridge this gap. . The proposed approach will provide radically new insights into the Finite Element Method and the Stochastic Finite Element Method. In particular, it will produce new solutions to industrial problems in Biomechanics and Robust Design. This has the potential of transforming personalised medicine and high-value manufacturing and will open the door to new industrial applications.

Facial prostheses are needed when patients are treated for certain cancers or accidental injuries affecting, for example, the nose, lips, eyes, ears, or skin. The quality of prostheses is naturally very important for patients, both protecting the affected area and giving them confidence, self-esteem, and an improved quality of life. The demand for facial prostheses is growing rapidly, with increases in cancer rates, an ageing population, and rising patient expectations. Within the UK, there are currently over half a million people with facial disfigurement, and each year about 2,500 new patients need facial prostheses. Compounding the problem, prostheses need to be renewed every 12-18 months as they degrade and discolour. At present the production of facial prostheses is technically demanding and lengthy, with the end-product depending on the skill of only a few highly experienced maxillofacial prosthetists. Their number is likely to diminish further with 20% of the workforce due to retire over the next 5 years. A new approach is needed urgently to deliver consistent high-quality prostheses to patients in a timely and cost-effective manner. There are, though, significant challenges. To date, no modern manufacturing method has managed to control medical grade silicone to reproduce facial skin tissue with the necessary softness, colour, surface texture, and flexibility, all in high fidelity. In fact, there is no good computer model for 3D facial skin appearance, even with the latest digital imaging techniques. To meet these challenges, we have brought together a multidisciplinary team of experts and early career researchers (ECRs) from five universities whose expertise is essential for a successful outcome: clinicians in maxillofacial and oral surgery, scientists and engineers in 3D printing (additive manufacture or AM), reconstructive science, biomaterials, colour science, and imaging. The multidisciplinary nature of this project will allow ECRs to gain broader knowledge, skills, and leadership training in different research areas, mentored by researchers at the forefront of their fields. Our work entails several innovations: - introducing 3D hyperspectral imaging and computer modelling of facial skin colour, texture, 3D shape, and translucency for all ethnicities - developing hybrid AM systems for manufacturing medical silicone parts with micron-level modelling of skin surface colour and texture - transforming physical modelling data to digital pipeline AM printer control - formulating new medical silicones and colorants with improved longevity - maintaining throughout a patient-centred approach, with patient feedback incorporated at every stage of the manufacturing process. The tight integration of these advances is central to achieving our goal, enabling the prompt delivery of bespoke ultra-realistic facial prostheses on demand. The results of the research will be delivered mainly through two NHS Foundation Trusts (Manchester University and Guy's and St Thomas', London) and will support regional NHS networks for prosthetic services and charities. We will work with local SMEs to facilitate sustainable research development and further investment. We will share our technological innovations with the clinical, scientific, and engineering communities, especially with developing countries with limited resources.

DATA-CENTRIC will fundamentally transform modern computational engineering through the development of algorithms that are accountable. This means algorithms capable of quantifying the uncertainty arising from computation itself, delivering simulations that are more transparent, traceable and at the same time more efficient. Crucial decisions in science, engineering, healthcare and public policy rely on established methodologies such as the Finite Element Method and the Stochastic Finite Element Method. However, the models that inform such decisions suffer from an inevitable loss of accuracy due to, and not limited to the following sources of uncertainty: a) time and cost constraints of running modern high-fidelity computer models, b) simplifying approximations necessary to translate mathematical models into computational models, and c) limited numerical precision inherent to any computer system. Therefore, there is a continuous risk of relying on unverified computational evidence, and the path from modelling to decision-making can be (inadvertently or unwillingly) obscured by the lack of accountability. DATA-CENTIC will solve this problem through Probabilistic Numerics, a framework that will enable decision-makers to monitor, diagnose and control the quality of computer simulations. Probabilistic Numerics treats computation as a statistical problem, thus enriching computation with a probabilistic measure of numerical error. This idea is gathering momentum, especially in the UK. However, theoretical development are still in their early stages and except for a few examples, it has not been applied to solve large-scale industrial problems. Consequently, it has not yet been adopted by industry. DATA-CENTRIC will bridge this gap. . The proposed approach will provide radically new insights into the Finite Element Method and the Stochastic Finite Element Method. In particular, it will produce new solutions to industrial problems in Biomechanics and Robust Design. This has the potential of transforming personalised medicine and high-value manufacturing and will open the door to new industrial applications.

TIDAL Mapping (WS1). Guided by our consultations with partners we will carry out a series of short focused projects. The first two will focus on the regulatory landscape for AT post-Brexit and will review successful translation of EPSRC-funded research into AT products and services. Three further short projects, focused on key barriers and enablers will be developed by the Network. TIDAL community development (WS2) will establish the Network and maintain inclusive engagement. A major activity will be running the Annual Symposia and Doctoral Colloquium, with the first focussed on Responsible Engineering. TIDAL Research (WS3). We fund up to eight research projects of up to £65k (aiming for 2 per theme) to interdisciplinary teams who have an excellent research hypothesis for solving a clear unmet need. There will be three steps of development: 1) an agenda setting workshop 2) targeted calls and a team building workshop (i.e. mini sandpit); 3) review and select proposals for funding. All research projects will have a business mentor and we will also support industry placements (2 months maximum) for academics, and encourage industry-funded placements into academia. Guided by initial consultations with partners we will begin the Network+ with three themes. 1) Responsible Engineering 2) Sensors and Data Science for Communication Aids 3) Design & Digital Manufacturing Systems (DMS) & Physical Devices. An additional theme will be added as TIDAL N+ grows. WS4: Network Education & Dissemination (WS4) : The TIDAL project is led by Holloway, who co-leads the £19.8million AT2030 Programme (www.at2030.org) and the Global Disability Innovation Hub (GDI Hub) Academic Research Centre. These initiatives already have excellent networks for communication and dissemination and TIDAL will take advantage of these. Specific activities will include the development of policy notes based on the work in WS1-3, engagement with local innovators, colleges and schools through hackathons and GDI Hub Live events themed to TIDAL N+.