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In a series of papers from 2003, Gibson (Maxwell Institute) and collaborators developed Bayesian computational methods for fitting stochastic models for epidemic dynamics. These were subsequently applied to the design of control programmes for pathogens of humans and plants. A first application concerns the bacterial infection Clostridium difficile in hospital wards. A stochastic model was developed which was instrumental in designing control measures, rolled out in 2008 across NHS Lothian region, and subsequently adopted across NHS Scotland. Incidence in Lothian reduced by around 65%, saving an estimated £3.5M per annum in treatment and other costs, reducing mortality and improving patient outcomes, with similar impacts elsewhere in Scotland. A second application concerns the spread of epidemics of plant disease in agricultural, horticultural and natural environments. Models developed in collaboration with plant scientists from Cambridge have been exploited by the Department for Environment, Food and Rural Affairs (Defra) and the Forestry Commission under a £25M scheme, initiated in 2009, to control sudden oak death in the UK, and by the United States Department of Agriculture to control sudden oak death in the USA.
Mathematical modelling of livestock infections and disease control policies is an important part of planning for future epidemics and informing policy during an outbreak of infectious disease. Researchers in the Mathematics Institute, University of Warwick, are considered to be at the cutting-edge of developing policy-orientated mathematical modelling for a number of livestock infections. Such models have been used to inform government policy for foot-and-mouth disease (FMD) and a range of other infections including bovine tuberculosis (bTB) and bee infections. From 2008, their work with responsible national and international agencies has focused on statistical inference from early outbreak data, formulating models and inferring parameter values for bTB infection spread within and between farms, developing predictive models of FMD outbreaks in the USA, and extending such models to areas where FMD is endemic. This research has helped to shape policy and determined how policy-makers perceive and use predictive models in real-time.
Research conducted at the University of Bristol between 2003 and 2012 on the ecology, epidemiology and control of parasitic flies and worms has improved animal health and welfare in the UK and is addressing a major constraint on global food production — animal disease, particularly in the context of climate change. These are some of the impacts:
The WinBUGS software (and now OpenBUGS software), developed initially at Cambridge from 1989-1996 and then further at Imperial from 1996-2007, has made practical MCMC Bayesian methods readily available to applied statisticians and data analysts. The software has been instrumental in facilitating routine Bayesian analysis of a vast range of complex statistical problems covering a wide spectrum of application areas, and over 20 years after its inception, it remains the leading software tool for applied Bayesian analysis among both academic and non-academic communities internationally. WinBUGS had over 30,000 registered users as of 2009 (the software is now open-source and users are no longer required to register) and a Google search on the term `WinBUGS' returns over 205,000 hits (over 42,000 of which are since 2008) with applications as diverse as astrostatistics, solar radiation modelling, fish stock assessments, credit risk assessment, production of disease maps and atlases, drug development and healthcare provider profiling.
RVC's Veterinary Epidemiology, Economics and Public Health team (VEEPH) has been at the forefront of applying and evaluating new techniques for modelling disease risk, for policy and decision makers to use in surveillance and control of animal and zoonotic infections. Application of their recommendations, including European `Commission Decision' legislation, is contributing to ensuring that Europe remains free from African swine fever (ASF). The status of FAO Reference Centre in Veterinary Epidemiology, awarded by the United Nations' Food and Agriculture Organisation in 2012, recognises the RVC as a centre of excellence in this field and reinforces its role in guiding policies relating to animal health.
Research carried out by Cairns (Maxwell Institute), Blake (Cass Business School) and Dowd (Nottingham, now Durham) in 2006 produced the `CBD' model for predicting future life expectancy. The CBD model and its extensions developed in 2009 by Cairns and collaborators have had a major impact on pensions and life industry risk management practices: multinational financial institutions [text removed for publication] and other stakeholders have relied on the CBD model to risk assess, price and execute financial deals [text removed for publication] since 2010. CBD is also used by risk management consultants to advise clients, is embedded in both open-source and commercial software, and is used by the UK's Pension Protection Fund to measure and manage longevity risk.
Meningococcal meningitis affects up to 100,000 people and causes around 10,000 deaths annually in the African `meningitis belt', a region of sub-Saharan Africa stretching from Senegal in the west to Ethiopia in the east. Dr Blyuss has developed a mathematical model that is able to explain the observed patterns of dynamics of this disease in terms of immunity and seasonality. This model is currently used by the Meningitis Vaccine Project to design optimal strategies for the control of meningococcal meningitis in the endemic areas, to inform specific public-health decisions regarding the deployment of the MenAfriVac™ vaccine, and to assess its effectiveness. Other epidemiologists, including those at the World Health Organization (WHO), are also using the model to improve public-health policies aimed at combating meningitis.
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.
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.
Impact: BEAST software has widespread applications with impacts on public health policy, service provision and awareness, and in other contexts such as commercial disputes and criminal cases.
Beneficiaries: Public agencies such as health bodies and criminal courts; ultimately, global and local populations subject to infectious disease epidemic and pandemic outbreaks in which BEAST is used to inform the response.
Significance and Reach: BEAST is critical software that has been used to understand the spread of and to inform the response to global pandemics such as H1N1 swine-flu. It is also used to determine disease origin and transmission issues in specific situations (e.g. in criminal cases). The reach of this software is therefore both global and local.
Attribution: Rambaut (UoE) co-led the phylogenetic research and developed BEAST with Drummond (Auckland, NZ). The subsequent epidemic and pandemic analyses were variously led by Rambaut and Pybus (Oxford) and by Ferguson (Imperial College London).