The Department has a long track record of working with a variety of industrial and public sector partners. This has produced wide ranging impacts on society, with the results of research in our Statistics group being most prominent. This is highlighted in the six Impact Case Studies submitted for REF2021.
Atom is a digital challenger bank based in the City of Durham, offering an innovative and efficient mobile-only app-based personal and business banking experience. One of the reasons Atom chose Durham as its base was to take advantage of the research output of the University. The collaboration between Atom Bank and Durham University has resulted in the creation of the Atom Bank Digital Twin developed within the Mathematical Sciences Department, in a project currently led by Professor Camila Caiado. This is an end-to-end banking model based on mathematical and statistical methods developed by the Durham Statistics group, providing strategic understanding of the relationships between the bank's key components, including customers and products.
The model supports major business decisions such as financial planning, resourcing, product pricing and funding. This has allowed Atom Bank to re-invent financial planning in financial services, enabling it to optimise business activity so that it generates the maximum return from each set of investment decisions, and to fully understand the resulting impacts of any business decision on each business area. Atom says that this was 'critical to building a competitive advantage that can be shared between investors and customers' through improved management of short-term liquidity requirements, improved capital deployment and operational risk reduction.
The UK government’s response to Climate change risks draws heavily on the uncertainty analysis for future climate outcomes carried out by the Met Office in their UK Climate Projections 2009 (UKCP09) and 2018 (UKCP18), both of which exploited fundamental research carried out by Durham University’s Statistics group into the Bayesian analysis of uncertainty for physical systems modelled by computer simulators. This work, led by Professor Michael Goldstein, developed a very general probabilistic framework for linking mathematical models to the physical systems that they purport to represent. This framework takes account of all sources of uncertainty, including model and simulator imperfections.
Through UKCP18 there is further impact of this Durham research on all those industries and public sector organisations which must address the uncertainties in future climate in order to make informed decisions on policy and investment.
Tempest ENABLE is an oil and gas reservoir simulator package sold by Roxar/Emerson. The ENABLE software predicts future reservoir performance and optimises field development for the oil industry by providing an emulation, history matching and uncertainty assessment system tailored to their needs. A key component is the statistical inference engine which was produced and subsequently improved by the Durham Statistics group, based on their research on uncertainty quantification for complex physical systems modelled by computer simulators.
Since 2012 Professor Ian Vernon has implemented recent advances which have resulted in an order of magnitude improvement in the software performance. This reduces development costs by accelerating the history matching process for oil reservoirs, resulting in vastly improved technical and economic decision-making. Professor Vernon's work has had substantial impact as ENABLE is currently used globally by more than 16 major oil companies.
Through research led by Professor Jochen Einbeck, Durham University's Statistics group have produced cutting-edge models, methods and software for estimating levels of radiation doses in those exposed in a radiological incident. This has resulted in the development of statistical methodology that goes beyond traditional procedures, and these new methods are referenced in an ISO standard and incorporated into the emergency response plans under RENEB, a European network specialising in radiation dose estimates. The team has developed an uncertainty quantification framework for the innovative gamma-H2AX protein biomarker. This enables quicker triage compared to other biomarkers, and hence greatly improved preparedness in case of a mass exposure scenario.
This has been adopted by Public Health England (PHE) into the standard operating procedures for its commercial biodosimetry unit. Through collaboration with PHE and European partners a BioDose tools package has been produced to simplify and standardise uncertainty estimation in biodosimetry globally.
Professor Peter Craig's research on risk assessment played an important role in the development of the European Food Safety Authority (EFSA) policy on the treatment of uncertainty in food-related risk assessment. For example, Professor Craig's work has resulted in new ways to combine data from multiple studies, to properly quantify uncertainties due to limited data and to incorporate quantitative expert judgements in the absence of data. This has helped increase the transparency of the decision-making process in relation to pesticides and other areas of EFSA’s responsibility.
Professor Craig is a member of the EFSA Working Group on Uncertainty. This has led to impact on a variety of EFSA scientific assessments, including dietary intake of sodium, environmental and economic risk from import of plants and plant-based pests, control options for Campylobacter bacteria in poultry farms, and the cumulative risk due to use of multiple pesticides in human food production.
A team led by Professor Camila Caiado collaborated with IBEX Innovations to develop methods to extend their technology portfolio and underpin their software only solutions 'Trueview®’ and their patented multi-absorption-plate technology. The project addressed two crucial issues: determining material composition from the X-ray image, and dealing with noise produced by scattered X-rays. Bayesian methods developed with Professor Goldstein and others in the Durham Statistics group provided methods to analyse the X-ray images to determine material composition, and quantify the uncertainties. These methods also used the scattered X-rays to aid the interpretation of the image, thereby producing better results at lower X-ray doses.
The project showed tangible benefits in several applications which enabled IBEX to develop products in quality control, security and, most importantly, medical markets. Their technology is now incorporated into CurveBeam, is being incorporated into Planmed products and further agreements with other Original Equipment Manufacturers are in development.