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Advanced technologies for data visualisation and data mining, developed in the Unit in collaboration with national and international teams, are widely applied for development of medical services. In particular, a system for canine lymphoma diagnosis and monitoring developed with [text removed for publication] has now been successfully tested using clinical data from several veterinary clinics. The risk maps produced by our technology provide early diagnosis of lymphoma several weeks before the clinical symptoms develop. [text removed for publication] has estimated the treatment test, named [text removed for publication], developed with the Unit to add [text removed for publication] to the value of their business. Institute Curie (Paris), applies this data mapping technique and the software that has been developed jointly with Leicester in clinical projects.
Now-casting is the prediction of the present, the very near future, and the very recent past. It has been developed within a research programme led by Lucrezia Reichlin at LBS. It is relevant because key economic statistics, particularly quarterly measures such as GDP, are available only with a delay. Now-casting exploits information which is published early and at higher frequencies than the target variable and generates early estimates before the offb01cial fb01gures become available.
Now-casting has signifb01cant infb02uence and impact. The techniques reported in this case study are in widespread use by central banks and policy institutions. Furthermore, this research has achieved successful commercial impact via Now-Casting Economics Limited.
Targeted Projection Pursuit (TPP) — developed at Northumbria University — is a novel method for interactive exploration of high-dimension data sets without loss of information. The TPP method performs better than current dimension-reduction methods since it finds projections that best approximate a target view enhanced by certain prior knowledge about the data. "Valley Care" provides a Telecare service to over 5,000 customers as part of Northumbria Healthcare NHS Foundation Trust, and delivers a core service for vulnerable and elderly people (receiving an estimated 129,000 calls per annum) that allows them to live independently and remain in their homes longer. The service informs a wider UK ageing community as part of the NHS Foundation Trust.
Applying our research enabled the managers of Valley Care to establish the volume, type and frequency of calls, identify users at high risk, and to inform the manufacturers of the equipment how to update the database software. This enabled Valley Care managers and staff to analyse the information quickly in order to plan efficiently the work of call operators and social care workers. Our study also provided knowledge about usage patterns of the technology and valuably identified clients at high risk of falls. This is the first time that mathematical and statistical analysis of data sets of this type has been done in the UK and Europe.
As a result of applying the TPP method to its Call Centre multivariate data, Valley Care has been able to transform the quality and efficiency of its service, while operating within the same budget.
Researchers in Cambridge have developed a data standard for storing and exchanging data between different programs in the field of macromolecular NMR spectroscopy. The standard has been used as the foundation for the development of an open source software suite for NMR data analysis, leading to improved research tools which have been widely adopted by both industrial and academic research groups, who benefit from faster drug development times and lower development costs. The CCPN data standard is an integral part of major European collaborative efforts for NMR software integration, and is being used by the major public databases for protein structures and NMR data, namely Protein Data Bank in Europe (PDBe) and BioMagResBank.
Through a close collaboration with Ford Motor Company, simulation modelling software developed at the University of Southampton has streamlined the design of the car giant's engine production lines, increasing efficiency and delivering significant economic benefits in three key areas. Greater productivity across Ford Europe's assembly operations has generated a significant amount [exact figure removed] in direct cost savings since 2010. Automatic analysis of machine data has resulted in both a 20-fold reduction in development time, saving a large sum per year [exact figure removed], and fewer opportunities for human error that could disrupt the performance of production lines costing a large sum [exact amount removed] each to program.
Recent food crises show the importance of having effective means of food identification and analysis. Many tests have been developed to monitor food, but analysis of the resulting data is highly problematic. Mathematical techniques developed by Dr Julie Wilson at the University of York allow complex mixtures to be analysed and interpreted. They have enabled the Food and Environment Research Agency (Fera) to maximize the information available from food testing, resulting in improved food safety and authentication worldwide, and underpin the analytical testing services delivered by Fera. The techniques have been incorporated into a bespoke Matlab based solution which is now routinely used by Fera's Chemical and Biochemical Profiling section in the specialist testing services which Fera provides across the food storage and retail, agri-environment and veterinary sectors to over 7,500 customers in over 100 countries. In addition, the techniques are used in Fera's research, supporting around £8M worth of work to develop a wide range of global applications including the determination of disease-related biomarkers, contaminant detection, food traceability and the development of drought- and disease-resistant crop varieties.
The security of data in printing and network environments is an area of increasing concern to individuals, businesses, government organisations and security agencies throughout the world. Mathematical algorithms developed at the School of Mathematics at Cardiff University represent a significant step-change in existing data security techniques. The algorithms enable greater security in automatic document classification and summarisation, information retrieval and image understanding. Hewlett-Packard (HP), the world's leading PC vendor, funded the research underpinning this development and patented the resulting software, with the aim of strengthening its position as the market leader in this sector of the global information technology industry. Hewlett Packard has incorporated the algorithms in a schedule of upgrades to improve the key security features in over ten million of their electronic devices. Accordingly, the impact claimed is mitigating data security risks for HP users and clients and substantial economic gain for the company.
Since 2008, statistical research at the University of Bristol has significantly influenced policies, practices and tools aimed at evaluating and promoting the quality of institutional and student learning in the education sector in the UK and internationally. These developments have also spread beyond the education sector and influence the inferential methods employed across government and other sectors. The underpinning research develops methodologies and a much-used suite of associated software packages that allows effective inference from complicated data structures, which are not well-modelled using traditional statistical techniques that assume homogeneity across observational units. The ability to analyse complicated data (such as pupil performance measures when measured alongside school, classroom, context and community factors) has resulted in a significant transformation of government and institutional policies and their practices in the UK, and recommendations in Organisation for Economic Co-operation and Development (OECD) policy documents. These techniques for transforming complex data into useful evidence are well-used across the UK civil service, with consequent policy shifts in areas such as higher education admissions and the REF2014 equality and diversity criteria.
Small area estimation (SAE) describes the use of Bayesian modelling of survey and administrative data in order to provide estimates of survey responses at a much finer level than is possible from the survey alone. Over the recent past, academic publications have mostly targeted the development of the methodology for SAE using small-scale examples. Only predictions on the basis of realistically sized samples have the potential to impact on governance and our contribution is to fill a niche by delivering such SAEs on a national scale through the use of a scaling method. The impact case study concerns the use of these small area predictions to develop disease-level predictions for some 8,000 GPs in England and so to produce a funding formula for use in primary care that has informed the allocation of billions of pounds of NHS money. The value of the model has been recognised in NHS guidelines. The methodology has begun to have impact in other areas, including the BIS `Skills for Life' survey.
Onchocerciasis (river blindness) is a debilitating disease of major public health importance in the wet tropics. The African Programme for Onchocerciasis Control (APOC) seeks to control or eliminate the disease in 19 countries. Accurate mapping of Loiasis (eye-worm) was a requirement for implementation of APOC's mass-treatment prophylactic medication programme in order to mitigate against serious adverse reactions to the Onchocerciasis medication in areas also highly endemic for Loiasis. Model-based geostatistical methods developed at Lancaster were used to obtain the required maps and contributed to a change in practice of APOC in a major health programme in Africa. Our maps are used to plan the delivery of the mass-treatment programme to rural communities throughout the APOC countries, an estimated total population of 115 million.